{"as_of":"2026-08-12T23:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:351de33eab6fd142a56388fee17740f3a6917805bbe87a720d4b560cc839e66b","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:13:35.456634Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.01787/citation-record","integrity":"/paper/2412.01787/integrity","json":"/paper/2412.01787/citation-record.json","paper":"/paper/2412.01787"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:38.320638Z","title":"Ac- curate structure prediction of biomolecular interactions with alphafold 3","venue":null,"work_id":"d1151e64-5ed3-4532-8d2d-0b9a35280430","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.045373Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:879f894cbc04e40f75ef16216515490d21d1fd97925294b7060efe54d15bb641","observation_id":"99306376-c015-45e6-a4a3-55f55aff946e","resolution":{"observed_at":"2026-08-12T10:13:38.430123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:38.300019Z","title":"Build- ing normalizing flows with stochastic interpolants","venue":null,"work_id":"33704e5d-0d9d-4043-831e-56bda468d256","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.102874Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:32d70a0b3e4ec3f8b05a63ef0ff0164b24fd10c781fe80b1b72149f683713cd8","observation_id":"ddbca9a6-b038-4d10-8a97-0a0e8aa0c40c","resolution":{"observed_at":"2026-08-12T10:13:38.310727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:38.075337Z","title":"All are worth words: A vit backbone for diffusion models","venue":null,"work_id":"ff95fcfb-7e28-4422-8be6-c6a6fc7a21e0","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.109256Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:cb41929610781e3abeeefa8032427a2e3f63e4eead4e6021df08649352784a23","observation_id":"91bcdde1-0605-46af-b58e-6f5bee2a8201","resolution":{"observed_at":"2026-08-12T10:13:38.205019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.03126","last_updated":"2022-03-16T01:35:49Z","snapshot_observed_at":"2026-08-12T01:15:35.596933Z","submitted_at":"2021-12-06T15:55:30Z","title":"Label-Efficient Semantic Segmentation with Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.03126","snapshot_observed_at":"2026-08-12T10:13:34.115204Z","title":"Label-efficient seman- tic segmentation with diffusion models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.115204Z"},"links":{"cited_paper":"/paper/2112.03126","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:94f7241ffa387295e4bd44bb5b34baa13f363b3d18a06fde47a4fbd3bbfdbcee","observation_id":"e2f8ad9a-079b-4b4d-aee0-f18a43f147bb","resolution":{"observed_at":"2026-08-12T10:13:34.115204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:38.059641Z","title":"Rep- resentation learning: A review and new perspectives","venue":null,"work_id":"a0f6006f-78f0-46ac-a6df-c2b0145cf793","year":2013},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.119998Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:ccab8de53397a407ac7d0aaf7342fdce0b428216f50a7249bc08eb5fd04e6694","observation_id":"fe084ef8-736b-44a0-a199-aadc1efa6e63","resolution":{"observed_at":"2026-08-12T10:13:38.064813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:38.034953Z","title":"Generalized denoising auto-encoders as generative models","venue":null,"work_id":"0c8fc88d-e6c0-4cb6-a141-947d6151e6d6","year":2013},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.125474Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:5da68c74130cf85d58765a9f537457d9b6b14ce73f19ae2c77c67b26346d9ed6","observation_id":"eb296319-bd0f-4f12-828b-660d917a0eff","resolution":{"observed_at":"2026-08-12T10:13:38.050283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.131854Z","title":"Improving image generation with better captions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.131854Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:e193ce66fbe36fdfca9890e3c88c1d0c0c1a880b82be6ff4790a02aed2359249","observation_id":"a7b91af9-a83d-4837-9d31-23b0e2771e63","resolution":{"observed_at":"2026-08-12T10:13:34.131854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.922054Z","title":"Diffusion models are certifiably robust classifiers","venue":null,"work_id":"94e4b193-6fc7-4585-b01e-f2fb161c4de0","year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.161848Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:f19a4861ec02c24e78492db1c856502cd956a50119661677c11d84115925817a","observation_id":"1216306f-85f8-4710-a12d-ca0492bfe18d","resolution":{"observed_at":"2026-08-12T10:13:37.962643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15241","last_updated":"2024-05-21T11:07:58Z","snapshot_observed_at":"2026-08-02T04:16:08.176028Z","submitted_at":"2023-05-24T15:25:19Z","title":"Robust Classification via a Single Diffusion Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15241","snapshot_observed_at":"2026-08-12T10:13:34.197623Z","title":"Robust clas- sification via a single diffusion model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.197623Z"},"links":{"cited_paper":"/paper/2305.15241","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:0d60b3ba4baf859d7c8c6a7718951d7fa20055539b6b4f63f83dafc9bc1d0d5b","observation_id":"3ac3dbd1-915f-4398-a5a8-f21f9acf839d","resolution":{"observed_at":"2026-08-12T10:13:34.197623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.811679Z","title":"Generative pretraining from pixels","venue":null,"work_id":"67cae088-e373-4880-b170-756900d5af70","year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.240387Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:83add4a06008eb272e948c78c75417ffec86a199b789cac56318586f00f21124","observation_id":"f0f36a30-f9fc-4e2e-8d64-cad2120a6a41","resolution":{"observed_at":"2026-08-12T10:13:37.853933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.794717Z","title":null,"venue":null,"work_id":"ee273ad4-f662-430d-9bbc-8dfb89aa1e7a","year":2018},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.247486Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:bf4fb04a50a49d8022efae99ba93ba62915e1cdaaee1ec39f646c64bd4ce25f4","observation_id":"98e6abaa-1e96-45c4-a3e7-652106aa9f9f","resolution":{"observed_at":"2026-08-12T10:13:37.800258Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.781141Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"1db15ae8-b40c-4f01-b0ad-fbe556428e99","year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.252539Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:3be1a17333ad31c48fa63b16f00a3e6d0ec965d3df629457992aeb10ee5e8cd6","observation_id":"b6b76e7f-2c96-4f67-a76c-c04452d0ec6c","resolution":{"observed_at":"2026-08-12T10:13:37.785689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.764542Z","title":"Infogan: Interpretable rep- resentation learning by information maximizing generative adversarial nets","venue":null,"work_id":"e72018dc-4477-4f4d-8bf9-ac202930d027","year":2016},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.283378Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:18f7061e7e7c4bde230dbaebe477619eea81ba2dd3ca74a563d37d3b87c39b69","observation_id":"b6ea3ddc-8cfe-422b-a806-091c29e61529","resolution":{"observed_at":"2026-08-12T10:13:37.770089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.328166Z","title":"Text-to-image diffusion mod- els are zero shot classifiers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.328166Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:d86fed7c388a894919c5af2a9a4f38cebfb83d726061c829e8dce9eff5887c13","observation_id":"a6ee16ca-486e-4dc6-868a-4361b1c15b01","resolution":{"observed_at":"2026-08-12T10:13:34.328166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.729852Z","title":"Autoaugment: Learning augmentation strategies from data","venue":null,"work_id":"c47c5354-bb86-49ca-b846-56f93b1c5a0b","year":2019},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.371252Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:20b88897c615a0b8d54c3146aa9243108ce3a57d981f5cd3484d4f61f5eeab59","observation_id":"b11c6766-5bb6-4206-b01e-ba5988360b64","resolution":{"observed_at":"2026-08-12T10:13:37.735332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.716365Z","title":"Large scale adversar- ial representation learning","venue":null,"work_id":"2216a857-08c6-4fe3-82e3-625b508a38a1","year":2019},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.398038Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7fc155728a9f532699c3142f36bb06195cf39a3624217d781991cacb64bd9fe8","observation_id":"c3e14430-ce42-42d7-aed5-929f922b26e2","resolution":{"observed_at":"2026-08-12T10:13:37.720059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-12T10:13:34.403849Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.403849Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:68a1d20bde0b6c5bdcbb024e58a221d9fa4557fc804a1393b9a340a9c14e2ee7","observation_id":"b9c43bbf-4f5a-404b-885a-c749f4a20351","resolution":{"observed_at":"2026-08-12T10:13:34.403849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.704062Z","title":"Your classifier is secretly an energy based model and you should treat it like one","venue":null,"work_id":"06e8590a-9ea7-4576-a50e-ec4280bed315","year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.408154Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:17ed4f00f6e53bedbe12d81d25343b289badb4ac964e2ed461cd78b1b52d69c8","observation_id":"62925ac4-442e-4761-9dd8-4e73cfe4bcae","resolution":{"observed_at":"2026-08-12T10:13:37.708578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.01171","last_updated":"2020-02-17T12:17:04Z","snapshot_observed_at":"2026-07-06T07:57:40.237841Z","submitted_at":"2019-06-04T02:56:14Z","title":"Understanding the Limitations of Conditional Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.01171","snapshot_observed_at":"2026-08-12T10:13:34.411974Z","title":"Understanding the limitations of conditional generative models","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.411974Z"},"links":{"cited_paper":"/paper/1906.01171","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:5b870a3c851450a98d8292bdf32f8810af0c3211f990160c56defcb745cfef04","observation_id":"a9959a3a-07bc-4917-9880-abc8b348618b","resolution":{"observed_at":"2026-08-12T10:13:34.411974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.3269","last_updated":"2015-04-10T21:05:32Z","snapshot_observed_at":"2026-08-09T10:14:51.937020Z","submitted_at":"2014-06-12T15:40:18Z","title":"Scheduled denoising autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.3269","snapshot_observed_at":"2026-08-12T10:13:34.416296Z","title":"Scheduled denoising autoencoders","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.416296Z"},"links":{"cited_paper":"/paper/1406.3269","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:8c5382d381f322a340b485e961a99c65d1f5d9ddd62b8c3f9e01b37f33a64f8b","observation_id":"8404fd35-6446-4c31-8bb3-8dc9fff867b6","resolution":{"observed_at":"2026-08-12T10:13:34.416296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.629329Z","title":"Generative adversarial networks","venue":null,"work_id":"0c98d948-f586-4d86-9293-c041a5c00144","year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.420344Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:e09e25c15d714be92996e810e2794ba215c987cc482dc28106258a41858cef1b","observation_id":"a3d43b95-1841-4d60-9cdb-7de077d58a2e","resolution":{"observed_at":"2026-08-12T10:13:37.664296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.538123Z","title":null,"venue":null,"work_id":"9e80c25c-09d2-478f-9e4f-9a3b5d7189e1","year":2019},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.425175Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7e95ea14964e3b6c4b16cb90c784d2ba3032b695334d9e688cfab2f1b01a7919","observation_id":"16e82cc0-d0f1-4b72-931d-92fe8988cbbb","resolution":{"observed_at":"2026-08-12T10:13:37.592590Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.499260Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"5c1778f2-dfaf-42cc-ab53-3bd9cb32e5ba","year":2016},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.429553Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:2d821946662bdf71dad0c91887f52e67fbca2ee18b46353cd0ac35a095690bac","observation_id":"06b18a7f-2803-4ccc-8f59-4140fdc671f1","resolution":{"observed_at":"2026-08-12T10:13:37.504153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.433901Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.433901Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:b9801899efaabbad9f6299d04e70643782b57e27e9e4207f9b79afa331238d18","observation_id":"1a3ceb24-7d44-44a5-bc81-8f1be35021b1","resolution":{"observed_at":"2026-08-12T10:13:34.433901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02781","last_updated":"2020-02-17T06:16:13Z","snapshot_observed_at":"2026-07-06T08:42:24.926713Z","submitted_at":"2019-12-05T18:18:10Z","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02781","snapshot_observed_at":"2026-08-12T10:13:34.462913Z","title":"Augmix: A simple data processing method to improve robustness and uncertainty","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.462913Z"},"links":{"cited_paper":"/paper/1912.02781","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:09259a7407483f4cfa83f6fb0425afda1fda728bfa6e0a318e7a2dd32558be8f","observation_id":"897755ec-2d21-4d6c-80e2-765c7b306de5","resolution":{"observed_at":"2026-08-12T10:13:34.462913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.540717Z","title":"Denoising diffu- sion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.540717Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:b6cd2355736b87e0e9b1b5795147dae2bfa9988d07a8df1c98b493768b091d91","observation_id":"880bf588-b18c-4084-9a73-ccaed8100cc5","resolution":{"observed_at":"2026-08-12T10:13:34.540717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.463358Z","title":"Video diffusion mod- els","venue":null,"work_id":"95da1eb7-d4f6-4d4b-8738-25877a912c3d","year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.569895Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:0a02880632a4b8cd95356d8631f785c7042632403503870893eccf599d73eedc","observation_id":"c357716c-b319-4ed0-bd0b-88126f38a10f","resolution":{"observed_at":"2026-08-12T10:13:37.470215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06505","last_updated":"2025-07-21T07:11:27Z","snapshot_observed_at":"2026-08-07T17:19:17.919601Z","submitted_at":"2025-03-09T08:16:19Z","title":"DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability","version":3},"cited_work":{"arxiv_id":"2503.06505","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.06505","snapshot_observed_at":"2026-08-12T10:13:35.662445Z","title":"DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability","venue":"cs.CV","work_id":"92a54db0-4039-4204-aeaa-375a7651e73a","year":2025},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.574028Z"},"links":{"cited_paper":"/paper/2503.06505","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:82efa9bf65355b40b2a66b38b6b9f9a8f0b0657398a145da5938e979ddc7c7a0","observation_id":"5cfb5592-14a6-4e02-8b8b-198edc65699a","resolution":{"observed_at":"2026-08-12T10:13:35.744357Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.446077Z","title":"Hutchinson","venue":null,"work_id":"12906340-5c74-4091-bcd4-4e1b2a41371b","year":1990},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.578709Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7460422ae0ce943e5926ab391bc7ac2fe689d06a8323e7f6b6b402d91ecbf078","observation_id":"f05e5e9b-8fae-4e9a-863d-96020b0c35e3","resolution":{"observed_at":"2026-08-12T10:13:37.450539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-12T10:13:34.582789Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.582789Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:c4c1bca4baa331c79bfa5bf18c2b1b36040caa93410e8b5f5f4d2f3163d4419b","observation_id":"01a5d2dc-00ab-4fbf-ba3b-ae79b1e5e436","resolution":{"observed_at":"2026-08-12T10:13:34.582789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T10:13:34.586854Z","title":"Adam: A method for stochastic opti- mization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.586854Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7695cad931e1c7e7246c0aa2b85fd2211d79a48736495d6699c7cbdacbbc1a62","observation_id":"f714a30d-b20d-40ff-b992-6da28fe52070","resolution":{"observed_at":"2026-08-12T10:13:34.586854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.428058Z","title":"On the effectiveness of adversarial training against common corruptions","venue":null,"work_id":"6a08f243-a167-4aea-8814-577a854ce6c6","year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.590129Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:34c86ba92f14a68975807760fc4448282cbcfb1e3d06117d3847b3049d95326f","observation_id":"59e0726f-9bd4-4064-bd0b-01fd6549435f","resolution":{"observed_at":"2026-08-12T10:13:37.435809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.594260Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.594260Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:f4b8fd4e4425cecf722fa102f48846e52af3b7e63703aff29172f0c7e3c13e8d","observation_id":"329e91be-a62b-40b6-90ca-c61bbd6887fb","resolution":{"observed_at":"2026-08-12T10:13:34.594260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.598727Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.598727Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:482c7af74422088a346cdb5b8aceb802c4a4a95a7875bcf0a8303380f3a1071d","observation_id":"0bd8f3bc-5dd1-4d9f-b5a5-cc98848073a8","resolution":{"observed_at":"2026-08-12T10:13:34.598727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.396911Z","title":"Your diffusion model is secretly a zero-shot classifier","venue":null,"work_id":"4e3f24ab-0f29-43e1-af36-f765d47fc73a","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.602849Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:33b518b66c4f2a7d714bedc116adf98b185f45053fa11169614482ab6ca2dbd3","observation_id":"0aeadaf4-538d-4311-9b17-558a03a0ed9b","resolution":{"observed_at":"2026-08-12T10:13:37.402214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.382189Z","title":"An application of the principle of maximum in- formation preservation to linear systems","venue":null,"work_id":"ed98bf66-ff6d-4a90-a1ce-5eb577fb7da9","year":1988},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.653168Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:d19cb55a81e54c95836c83a787367578f0fea558537a045558cd03b71cc79440","observation_id":"af2ed541-f0a3-4157-b343-5c64c8a3ebe9","resolution":{"observed_at":"2026-08-12T10:13:37.387074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.291460Z","title":null,"venue":null,"work_id":"d6d8d32e-4956-49d9-a9b1-3caccb5f99e1","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.670619Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:90f8f8c2092670250500206087ce9f91131e7ae480175ef0b887291be6701b34","observation_id":"ad544089-a5d9-401e-994e-2d425e847916","resolution":{"observed_at":"2026-08-12T10:13:37.321722Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.228843Z","title":"Towards robust neural networks via random self- ensemble","venue":null,"work_id":"af07326e-1847-4ca5-a6b3-562c7e235dd4","year":2018},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.674734Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:6057f87f5fd8e3e7b154a660ffcad07c7405624c1fdfaf2cc5df7d5f54ecb5cb","observation_id":"95f08782-6570-4d77-be2f-f35628ec4c4f","resolution":{"observed_at":"2026-08-12T10:13:37.251153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-12T10:13:34.680048Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.680048Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:e67305513d89b54339a55cfd1e21c70c425528f0ec8ad56e2db89cc8d8555347","observation_id":"59e0cf18-1044-4bae-8788-4ec0526a4501","resolution":{"observed_at":"2026-08-12T10:13:34.680048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.683931Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.683931Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:2d50c9e0f2c7e1f27936bb1cbfeab4a5a45e6fb7c40b22aaa4416c8c9c376c1b","observation_id":"ccd84399-b914-4c4c-bd18-d999ceafb7cd","resolution":{"observed_at":"2026-08-12T10:13:34.683931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.175692Z","title":"Good helper is around you: Attention-driven masked image modeling","venue":null,"work_id":"ea6c1ba3-da07-4166-a466-490e22223ba6","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.687987Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:4e56235e44bd098589392df05d5969051ff2fb1510454f1bd17e8e888701496f","observation_id":"8b22e4c1-4311-4bb7-aff6-ac1b6ad7d694","resolution":{"observed_at":"2026-08-12T10:13:37.182307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.158926Z","title":"Maximum likelihood training for score- based diffusion odes by high order denoising score matching","venue":null,"work_id":"7013bc1b-e790-4592-a60f-b0dcbcd8b843","year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.692687Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:b8a675250fffaa94dfdebff20aa326fed0e39106e55502493d36bcc75720db8e","observation_id":"063a14fd-439b-47f1-9caa-257e0193ab76","resolution":{"observed_at":"2026-08-12T10:13:37.164156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08740","last_updated":"2024-09-23T15:59:41Z","snapshot_observed_at":"2026-08-10T16:29:09.535299Z","submitted_at":"2024-01-16T18:55:25Z","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08740","snapshot_observed_at":"2026-08-12T10:13:34.697417Z","title":"Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.697417Z"},"links":{"cited_paper":"/paper/2401.08740","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:149caecd84654d80d4ce0e8428ce9362a87a29b8bac91258b56af38c9bce236c","observation_id":"93bc4d03-8831-491c-8114-c742f9a50634","resolution":{"observed_at":"2026-08-12T10:13:34.697417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-12T10:13:34.738436Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.738436Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:5bd2519669cc5070d7004f64a9b87d7a3ba1de0a0743aadbe01811e421c37306","observation_id":"b1057d45-f9e7-475d-b557-2d4f5704b360","resolution":{"observed_at":"2026-08-12T10:13:34.738436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16091","last_updated":"2021-11-25T19:58:39Z","snapshot_observed_at":"2026-08-04T23:36:11.662435Z","submitted_at":"2021-03-30T05:48:05Z","title":"Symbolic Music Generation with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16091","snapshot_observed_at":"2026-08-12T10:13:34.780039Z","title":"Symbolic music generation with diffusion models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.780039Z"},"links":{"cited_paper":"/paper/2103.16091","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:46ea4c41007cbceacda18542a10d27f5778566d1471cb7770e87d348f500e36f","observation_id":"4d929b00-36f4-40c3-91d6-d87bdda9dfb6","resolution":{"observed_at":"2026-08-12T10:13:34.780039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.141410Z","title":"Diffusion based represen- tation learning","venue":null,"work_id":"f506cdfe-aec4-481e-8b2c-a2cdbd059442","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.792956Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:a5b317b7088563ebbd5d3efca47ced3159f96b837f32f504cbc34206647a5592","observation_id":"d49c4566-6546-4135-b59f-7fa44476d5b7","resolution":{"observed_at":"2026-08-12T10:13:37.147887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.125148Z","title":"Probabilistic machine learning: Advanced topics","venue":null,"work_id":"3c86abb7-b1fc-4275-91e9-794a8764f7b2","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.797696Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:9330129f1490f3657c9a67b95cf56edcbb920aa7ea41626946d1772cfbec9c76","observation_id":"d312f3e3-9b65-4784-8c57-f1fd30dabb40","resolution":{"observed_at":"2026-08-12T10:13:37.129980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:37.106838Z","title":"On discriminative vs","venue":null,"work_id":"1aa59718-989a-4d88-ae52-1365397c572c","year":2001},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.803024Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:1ae50f056a661c12b1a3d45bbedc8a0b43168e009c887bbde099f468b2a11d8f","observation_id":"367040f4-e371-408a-bb3e-57e0d7546341","resolution":{"observed_at":"2026-08-12T10:13:37.114441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.807007Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.807007Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7edf6c153131eba53e4e2e4bc9edfdad89b5a14fa956e5840dbd2406f5631743","observation_id":"3d670aac-4632-4cbe-b763-995169ba6322","resolution":{"observed_at":"2026-08-12T10:13:34.807007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.995073Z","title":"Diffusion models for adversarial purification","venue":null,"work_id":"84cfcda2-649d-420a-ac06-9c51133723c1","year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.810820Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:2c3f1ae04af4c675414e2027f8e2c2caa83b9be79949163b601980ed91b903fa","observation_id":"06c74cc8-6c82-41e5-a2df-7a57a899b470","resolution":{"observed_at":"2026-08-12T10:13:37.076219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.793971Z","title":null,"venue":null,"work_id":"78e61af9-fbcf-46cf-ac42-9b1ffddf62da","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.816147Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:93fa8661aa4fa2e0fe395a0be5353e2632961f054ee6629c865b65af03524b08","observation_id":"bdfe2161-88aa-4fba-83c8-fd081d8802e5","resolution":{"observed_at":"2026-08-12T10:13:36.897981Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-12T18:43:14.431633Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-12T10:13:34.831132Z","title":"Unsupervised representation learning with deep convolutional generative adversarial networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.831132Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:9f123321ee78ec1c3539add6f0e193571d7a801f5ba2b4694c239de3065d7ebd","observation_id":"d625ae55-8e61-4477-8267-3967ae1b68a6","resolution":{"observed_at":"2026-08-12T10:13:34.831132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.781116Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"91c8bf83-93c1-44da-ada6-2f61387baf2e","year":2022},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.877945Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:3be47360a55f953deb23c752c69aac914218343ee213b6c021b9255269cd781e","observation_id":"cd93fbce-b711-45bd-93b3-7af86a5577c0","resolution":{"observed_at":"2026-08-12T10:13:36.785781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.767765Z","title":"Bernstein, Alexander C","venue":null,"work_id":"f19c7f93-6c5a-46c5-a23f-b22304e60707","year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.931057Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:8cb1b2de8682d0de98dce62c613fd0943584be78937e71501b832c115d97756f","observation_id":"2c4aeef7-d4cb-4f49-9d61-2c8fd1595e56","resolution":{"observed_at":"2026-08-12T10:13:36.772298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.754433Z","title":"Rethinking the spatial inconsistency in classifier- free diffusion guidance","venue":null,"work_id":"18a85550-070b-41ac-9b92-681faad4266f","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.966932Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:a2f3302925086ac703ce6459263a5b04a16e22b0e272d9aac6476368dccc91f0","observation_id":"0305ff6a-e4c0-4fd4-8daa-236efa98f54b","resolution":{"observed_at":"2026-08-12T10:13:36.758615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.740229Z","title":"D2c: Diffusion-decoding models for few-shot con- ditional generation","venue":null,"work_id":"5b309580-1c4b-43e3-95ca-a8981ab1d508","year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.972251Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:7938477452e47b23e51c15b39ae18a7921494574ed86b77a88991582fed9a84b","observation_id":"231e5eb7-30d2-41b2-b385-2b303aa5344c","resolution":{"observed_at":"2026-08-12T10:13:36.745684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.610545Z","title":"Deep unsupervised learning using 10 nonequilibrium thermodynamics","venue":null,"work_id":"e4fafd0d-2a33-4698-bd74-c37174c6171c","year":2015},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.976375Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:06dc1db59bb7f4ee94d0db0679339492f8231a5a62879188a1821df9b6797c59","observation_id":"1e55cf2a-a1b9-445a-89f6-024df9e3d2b9","resolution":{"observed_at":"2026-08-12T10:13:36.700817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-12T10:13:34.979816Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.979816Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:a640be3cad92ed1b2e183f9cad44250962e8c9305243f067f000d7905d4552e6","observation_id":"47eb793b-c451-44ec-88fc-722ff7b53ce0","resolution":{"observed_at":"2026-08-12T10:13:34.979816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.563265Z","title":"Maximum likelihood training of score-based diffusion models","venue":null,"work_id":"967e7c20-cad7-45bc-870a-fa245c4d3a7d","year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.984106Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:2333fdd3ee3b7ff9bc0c0d59614553c42e8de032cf46db468ba89b4a974991da","observation_id":"718e6cb8-f299-4004-b99f-c3116fa3681e","resolution":{"observed_at":"2026-08-12T10:13:36.567289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.547855Z","title":"Improving and generalizing flow-based gener- ative models with minibatch optimal transport","venue":null,"work_id":"5f66c3dd-b237-4ab1-86f2-76267a1e6d4d","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.990403Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:9fdb22b31aa79eae0c14848e7e0c95e7a39105221318e3bf664b417dd9c499cf","observation_id":"b06217e7-2d7d-41a7-b96e-e4c99a44794f","resolution":{"observed_at":"2026-08-12T10:13:36.554831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:34.995764Z","title":"Neural discrete representation learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:34.995764Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:2cfaff0d4af8d7742685ca97e6e73bbb3976e37bafc81de92926dabf03887d21","observation_id":"be8401e1-0b74-4c2a-926b-6bb64ef1beb4","resolution":{"observed_at":"2026-08-12T10:13:34.995764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.517089Z","title":null,"venue":null,"work_id":"870e2647-97af-434d-9090-0a5e0b169d58","year":1995},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.000017Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:6f3b08dd80c89ed07fd4b85d6a662a9a1f76dfa0527137e14f074bfa18f49d34","observation_id":"bfb198c3-2837-4a87-bf4a-8452b10f58c1","resolution":{"observed_at":"2026-08-12T10:13:36.521470Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.425143Z","title":"A connection between score matching and denoising autoencoders","venue":null,"work_id":"6d60bd17-daec-42f0-99d4-dae582c4709f","year":2011},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.003991Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:a6587870af865b2c62196001ba3be5d4eedce7c80cf083e0050242ea784a676f","observation_id":"31d4fdc7-d5ed-4298-bb6a-7ff772253f8e","resolution":{"observed_at":"2026-08-12T10:13:36.506721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.410244Z","title":"Extracting and composing robust features with denoising autoencoders","venue":null,"work_id":"9431194e-e610-4a07-a4a9-060f1a3a374d","year":2008},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.008278Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:8a679153c68a0ce71f6ab9889afb35c101cb56cf4f28ee456b64add96bc1939a","observation_id":"a4bab967-2be4-43cf-a6b7-4cb546e12977","resolution":{"observed_at":"2026-08-12T10:13:36.414903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.396428Z","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","venue":null,"work_id":"e6b286c0-3f81-41ca-89ef-bb004830c49e","year":2010},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.012854Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:b5040f82e3983f46a016f01dd2cbc874cbbb2c5546f3205a59c68a0d8df80165","observation_id":"4194f76e-4928-4a21-b308-9a6e1ae40491","resolution":{"observed_at":"2026-08-12T10:13:36.401014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.307412Z","title":"Enresnet: Resnets ensemble via the feynman–kac formal- ism for adversarial defense and beyond","venue":null,"work_id":"268a301d-cd6d-4a8c-b94c-7ff9ccb4fcf1","year":2020},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.021919Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:02aa52dd6548937ead8143b1aae1bcc6df4a11e33c8bfff085cdb432c735416f","observation_id":"52ccbcd0-a212-4e46-81f8-a706d4e5cfbe","resolution":{"observed_at":"2026-08-12T10:13:36.387865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.249718Z","title":"Phased consis- tency models","venue":null,"work_id":"463172e0-1626-4135-befb-c3af7f7ee5b2","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.097623Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:eab98762570d857f0b712971130b2604fbb939dc9893cdea1f2f226db0be80f1","observation_id":"269f1e60-1e48-485e-85bd-3f33fd95e987","resolution":{"observed_at":"2026-08-12T10:13:36.254023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.233899Z","title":"Denoising diffusion autoencoders are unified self-supervised learners","venue":null,"work_id":"5bd78841-77ec-44c0-adc7-3e9a1dea66aa","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.158397Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:3825955cbde8fdb674eaa657332428bd1f8f6b2b4243a3dd0f5da395e6a24dd5","observation_id":"cb15cbef-b558-4969-adff-25a97e9d285c","resolution":{"observed_at":"2026-08-12T10:13:36.240092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:35.162748Z","title":"Aggregated residual transformations for deep neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.162748Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:109e35b5a0384709f56946edaa688ec1dacbbac853b8feda8b617e2a3fb87938","observation_id":"f9c60d04-f693-4b7d-a355-41cbaf3cc278","resolution":{"observed_at":"2026-08-12T10:13:35.162748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.07791","last_updated":"2022-08-16T15:02:21Z","snapshot_observed_at":"2026-07-06T13:42:20.890216Z","submitted_at":"2022-08-16T15:02:21Z","title":"Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.07791","snapshot_observed_at":"2026-08-12T10:13:35.174119Z","title":"Your vit is secretly a hybrid discriminative- generative diffusion model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.174119Z"},"links":{"cited_paper":"/paper/2208.07791","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:5d22bda205bca39405ea1f1ff9e635ea389b0216b6d4bbbbf936abdb50d5d5b6","observation_id":"ee9ec43f-4c97-496b-8e8b-9c50896b3d6c","resolution":{"observed_at":"2026-08-12T10:13:35.174119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.205247Z","title":"Pde+: Enhancing gen- eralization via pde with adaptive distributional diffusion","venue":null,"work_id":"be359464-917a-4146-b3c5-4db9f00cfa76","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.178427Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:84a01a76b8afff326e43d4627c1eae17696dff0076c596290367b286550e8a9a","observation_id":"9901600d-48c3-4893-a323-b61ba86e6f69","resolution":{"observed_at":"2026-08-12T10:13:36.210753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:35.181969Z","title":"Cutmix: Regu- larization strategy to train strong classifiers with localizable features","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.181969Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:d07f78e77fec5ff9f45b19df99b663fd3fa8be10b4e6deac546ccd977610d5f6","observation_id":"c759b6b4-2895-48f7-8851-9b4f9549abd2","resolution":{"observed_at":"2026-08-12T10:13:35.181969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-08T14:57:17.868613Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-12T10:13:35.186541Z","title":"Wide residual networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.186541Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:d716dded34e158dad37d77316c231a8b089fd7f0e36d863f00798b043d7c4dbc","observation_id":"5cb77837-0654-45cb-83ac-92c9b80280dc","resolution":{"observed_at":"2026-08-12T10:13:35.186541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-12T10:13:35.191558Z","title":"mixup: Beyond empirical risk minimization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.191558Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:bd9815da1f24de5a2910c921d6faa43ed610c0b800755d56cb652b71d4e1d0c1","observation_id":"14cccf3f-5da5-4517-8f34-8d269a5b6e59","resolution":{"observed_at":"2026-08-12T10:13:35.191558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:36.182997Z","title":"Revisiting generative poli- cies: A simpler reinforcement learning algorithmic perspec- tive, 2024","venue":null,"work_id":"9a67f540-2043-40f8-a982-4d4cc302c3a5","year":2024},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.296181Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:4b084bc9757a45082454092bd92434f309e3054f59de0fd19035bacc866d7b33","observation_id":"bf28d8cc-d3a8-4e49-9e76-80728d1e9183","resolution":{"observed_at":"2026-08-12T10:13:36.187616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:13:35.979329Z","title":"Im- proved techniques for maximum likelihood estimation for diffusion odes","venue":null,"work_id":"b67c5b8d-5cbd-46df-ad19-1514c98b3ac7","year":2023},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.412192Z"},"links":{"citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:e39511d3c4dc2633fefbe64ffecfe7985a42e82799caf9657f8cc0c1e1eb0b31","observation_id":"3f2a8f8f-41a7-4e5f-8c8c-6f75b04bcd38","resolution":{"observed_at":"2026-08-12T10:13:36.079069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.00473","last_updated":"2021-12-11T11:51:43Z","snapshot_observed_at":"2026-07-06T11:53:31.073250Z","submitted_at":"2021-10-01T15:05:33Z","title":"Score-Based Generative Classifiers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.00473","snapshot_observed_at":"2026-08-12T10:13:35.456634Z","title":"Score-based generative classifiers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning","version":6},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:35.456634Z"},"links":{"cited_paper":"/paper/2110.00473","citing_paper":"/paper/2412.01787"},"observation_digest":"sha256:b9e5567dcc1e3f9222f57b7c68a56be1ac974bd7003a4e3322c83ae64110dae5","observation_id":"ad662e29-3f47-4056-891e-f55136a8b94a","resolution":{"observed_at":"2026-08-12T10:13:35.456634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.01787","last_updated":"2025-08-13T16:36:41Z","latest_version":6,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T22:41:00.563384Z","submitted_at":"2024-11-29T08:24:49Z","title":"Pretrained Reversible Generation as Unsupervised Visual Representation Learning"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":1,"verified_fuzzy":42},"total_outbound_references":77},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2412.01787."}