{"as_of":"2026-08-11T03:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b3b5e841725ba4fd57154d4b9a307138957412948e3d051bb1ac408048e3c87d","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:22:24.594618Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2501.15431/citation-record","integrity":"/paper/2501.15431/integrity","json":"/paper/2501.15431/citation-record.json","paper":"/paper/2501.15431"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:22:25.200373Z","title":"ImageNet Classification with Deep Convolutional Neural Networks,","venue":null,"work_id":"cc0ae513-ae0a-4dcc-b9cb-25e2338d4a25","year":2012},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.373202Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:a8c985baafd1971a0cd4d73c2c70d4cbaa911b318d8bf8a378b793038d9bfa99","observation_id":"15b4526a-9bce-4153-9cf2-79b78bb19fd9","resolution":{"observed_at":"2026-08-10T14:22:25.203303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.192064Z","title":"Very Deep Convolutional Networks For Large-Scale Image Recognition,","venue":null,"work_id":"517e3812-1289-4f35-9cf8-81bc6779f002","year":2015},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.377979Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:a22bf593c1c82a287baa37e8d3ac588923bfef01c88a6828c1ff4e2f555bb3fa","observation_id":"d709dcd8-1c9b-4edf-9d48-d6605a2af38f","resolution":{"observed_at":"2026-08-10T14:22:25.194887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.183629Z","title":"Aggregated Residual Transformations for Deep Neural Networks,","venue":null,"work_id":"152467a9-deab-4ea0-a97a-445f4a87c713","year":2017},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.382017Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:515482bd85cd9536e5b54d3db1dec2551e67748f3836829a3fd320e9e22e6927","observation_id":"43dd0b30-7ad8-43c6-a99a-0cda7cfb9be2","resolution":{"observed_at":"2026-08-10T14:22:25.186388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.175089Z","title":"Data Labeling: An Empirical Investigation Into Industrial Challenges and Mitigation Strategies,","venue":null,"work_id":"217fdbe1-a48a-4951-87c4-860d1d1cde5e","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.386303Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:8970e5914f72a439b7f80e69745dfd6ff51f588cd4dfb7055f5bdd6afc0102d9","observation_id":"75e8f294-47d7-462c-a489-6ef84b802de9","resolution":{"observed_at":"2026-08-10T14:22:25.178107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.167014Z","title":"Generalizing From a Few Examples: A Survey on Few-shot Learning,","venue":null,"work_id":"44e27515-51fa-4385-898b-ac78540302ca","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.390415Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:fb4c8a2c94741fa4949b02241021ba93d3ef4bab3b7707d3a63ba284f832aaf5","observation_id":"dead36ba-9b3b-442d-9798-f4e016a2ff4e","resolution":{"observed_at":"2026-08-10T14:22:25.169893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.157773Z","title":"A Survey of Transfer Learning,","venue":null,"work_id":"e5ace545-e6c4-4d18-8bab-dfda86b5e2ff","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.394257Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:c74aee3fe007df1f7a4cb69b59b6d5eaa5c6f1f8140c7f0dd1efb0b0efb1b055","observation_id":"ad95a6c8-1fe0-449a-bdb9-684dd051cf47","resolution":{"observed_at":"2026-08-10T14:22:25.161182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.148117Z","title":"Automatic Differentiation in PyTorch,","venue":null,"work_id":"6d8899c9-4038-412a-94af-37dfbd44f75d","year":2017},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.398400Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:dacd557b7712d70a050f9fdb7ec26557ff87999b6698182d3ae6622b6037cd5f","observation_id":"db52ade0-afa7-44f1-a734-a92509ddf72c","resolution":{"observed_at":"2026-08-10T14:22:25.151366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.138678Z","title":"ImageNet Large Scale Visual Recognition Challenge,","venue":null,"work_id":"cf6d3b80-7fdc-46f5-b26f-d6ebcba641fd","year":2015},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.402121Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:167d54c5c75f00871571a460405acacca1e57d3f369df941a192ff2a886b17c1","observation_id":"619092d3-ec33-40b0-87d4-f672da856794","resolution":{"observed_at":"2026-08-10T14:22:25.142113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.129253Z","title":"A Simple Frame- work for Contrastive Learning of Visual Representations,","venue":null,"work_id":"57f3347e-85c9-4361-b1f3-1bd4e4a858fa","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.405726Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:bad78791db3a3613de5ad68991b1ddc651fd82dd3d92f72a5dc5805ad7152f9f","observation_id":"4ae47e77-06f4-4d38-8a2c-bc148c755955","resolution":{"observed_at":"2026-08-10T14:22:25.132960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.119556Z","title":"Bootstrap your own latent-a new approach to self-supervised learning,","venue":null,"work_id":"1848fd8d-35f9-48f0-84dd-1767da8d1267","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.409237Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:17acb95dcaeda9086c5ebedf4a3581c08dd33a9233f1907fd88b78139baafcb4","observation_id":"3d9b58dd-9f8a-4ec9-9f4c-5c0acd17e0f1","resolution":{"observed_at":"2026-08-10T14:22:25.123226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.109336Z","title":"Momentum Contrast for Unsupervised Visual Representation Learning,","venue":null,"work_id":"8f4313e1-973a-49bc-b244-ea1b42f17139","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.413136Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:8ef0693a4a5a4116cf5633875fd585d63b36922645a90f5f33cc21a713102f13","observation_id":"ed40e5c9-447a-4219-98d0-33f8c591af39","resolution":{"observed_at":"2026-08-10T14:22:25.113365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04906","last_updated":"2022-01-28T12:23:37Z","snapshot_observed_at":"2026-07-06T11:08:17.373935Z","submitted_at":"2021-05-11T09:53:21Z","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04906","snapshot_observed_at":"2026-08-10T14:22:24.417491Z","title":"Vicreg: Variance-Invariance- Covariance Regularization for Self-supervised Learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.417491Z"},"links":{"cited_paper":"/paper/2105.04906","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:0223a2231e724ab304ecaa7abc704f9e183ffad3bb1ff3b25ada21e2afbdb6e0","observation_id":"f9e27c51-f766-4f70-b9a5-14b79b5f0ee8","resolution":{"observed_at":"2026-08-10T14:22:24.417491Z","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":"2026-08-10T14:22:25.099192Z","title":"Barlow Twins: Self-supervised Learning via Redundancy Reduction,","venue":null,"work_id":"1eb58145-a7ea-408b-b05a-ddbd65422280","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.421838Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:25a5cbfe879a7fbfc24d6939c8a20adf7caaf2987c5a2d9c091e33ebdaca00a1","observation_id":"16fd25ac-3233-49ee-a9da-ad173b60d3b9","resolution":{"observed_at":"2026-08-10T14:22:25.103188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.090241Z","title":"Prototypical Contrastive Learn- ing of Unsupervised Representations,","venue":null,"work_id":"3fa9260f-bf5c-4a05-b276-9770a1504405","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.425540Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:9a442cc893e3df701ad92cd6f4f59507c43d3e32f2a52e06b255c05d2452071b","observation_id":"da9911df-b2ab-49d9-af8e-f196967b4e52","resolution":{"observed_at":"2026-08-10T14:22:25.093364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.081390Z","title":"Unsupervised learning of visual features by contrasting cluster assign- ments,","venue":null,"work_id":"3681c1e8-6d7f-4274-8775-e3b0908bcb34","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.429097Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:d25f014ec1ffa3228c1f9c153a50d77c86add70e75838e47ac92c5170ad0c8d9","observation_id":"c5da504e-e401-4854-96e2-85b666cd0a67","resolution":{"observed_at":"2026-08-10T14:22:25.084546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.072130Z","title":"Self-supervised Learning of Pretext- invariant Representations,","venue":null,"work_id":"75d66459-4bc1-4e84-8566-81aa4f8ce1f5","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.432588Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:3e8d42cb5b36c6456766ba7d4daf0661a4a67cea3df786457dd8ed345bf1b928","observation_id":"ff23171b-3d3d-460d-9992-4f78cb7d245e","resolution":{"observed_at":"2026-08-10T14:22:25.075490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12210","last_updated":"2023-06-28T14:15:22Z","snapshot_observed_at":"2026-08-09T08:10:39.086440Z","submitted_at":"2023-04-24T15:49:53Z","title":"A Cookbook of Self-Supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12210","snapshot_observed_at":"2026-08-10T14:22:24.436023Z","title":"A Cookbook of Self-Supervised Learn- ing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.436023Z"},"links":{"cited_paper":"/paper/2304.12210","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:8654582e51581a4eead485f47f45269b34ab54324bbac3156d766d4b303acd6c","observation_id":"c33a5f39-6444-48f2-9312-f09a1412c5e1","resolution":{"observed_at":"2026-08-10T14:22:24.436023Z","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":"2026-08-10T14:22:25.063086Z","title":"Know Your Self- supervised Learning: A Survey on Image-based Generative and Discrim- inative Training,","venue":null,"work_id":"5451e5ca-f40e-4b6e-8719-2dd976dd0c22","year":2023},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.440169Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:19b7abd474499a30444023c8bc850a823c426dcbbba2845d76cc448627b0ee2c","observation_id":"cc94b233-849b-45f7-bfd0-f514bdcf63ca","resolution":{"observed_at":"2026-08-10T14:22:25.066166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.054464Z","title":"A metric learning reality check,","venue":null,"work_id":"a91d9477-eaab-4f07-9b17-dad18065424c","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.443751Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:d8b78b99b30cdd0314d0f5e832486f585aa4fb2f148379ad8bafca4e646f87de","observation_id":"9c6c4aa6-b20e-46f7-ae28-0e4e23fd26df","resolution":{"observed_at":"2026-08-10T14:22:25.057543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07002","last_updated":"2021-07-14T21:08:30Z","snapshot_observed_at":"2026-08-11T02:56:19.098865Z","submitted_at":"2021-07-14T21:08:30Z","title":"The Benchmark Lottery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07002","snapshot_observed_at":"2026-08-10T14:22:24.447379Z","title":"The benchmark lottery,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.447379Z"},"links":{"cited_paper":"/paper/2107.07002","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:94731706a132705031c282a511af695a107fc10464a47adaaa51cb05999730f3","observation_id":"3ebc5f37-7080-40ef-80bf-494a754f1730","resolution":{"observed_at":"2026-08-10T14:22:24.447379Z","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":"2026-08-10T14:22:25.045826Z","title":"An Empirical Study of Training Self- supervised Vision Transformers,","venue":null,"work_id":"d5bd6fa0-422a-41f5-bb82-236c1d3365be","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.451196Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:646c3e3ae1522ecb5bd96cef03a2067a68594b1006ebea748f8b31546d00c112","observation_id":"20f69358-be6d-46e7-83de-a121d766edea","resolution":{"observed_at":"2026-08-10T14:22:25.048800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.037323Z","title":"Self-organizing neural network that discovers surfaces in random-dot stereograms,","venue":null,"work_id":"513829c6-7673-4a0d-80b5-736ac29aca99","year":1992},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.454689Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:98a8f27c36b5b985f1842b5b860da183c0ce0c7c7ca6d784da3e5113199e7ce1","observation_id":"e409727c-9194-431b-b9b5-c44387572364","resolution":{"observed_at":"2026-08-10T14:22:25.040231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.027350Z","title":"Learning classification with unlabeled data,","venue":null,"work_id":"7f37899d-a8f4-4314-9e40-459fb5df10c0","year":1994},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.458092Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:5fb02c9ccb465caeac76033d802565a9175c02f274161d6e28f5259c521f1a9f","observation_id":"f80feed7-643f-4fa3-a08d-66494922df42","resolution":{"observed_at":"2026-08-10T14:22:25.031453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.017963Z","title":"Colorful Image Colorization,","venue":null,"work_id":"c14da6cf-d515-4146-a43e-00a2be451d0a","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.461382Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:687759afd4949e697d6d0f622250191e462c8bbf643f52ea258aee9aa65a3d3b","observation_id":"51f9ee3c-ab4a-43ef-8997-08f317504868","resolution":{"observed_at":"2026-08-10T14:22:25.021297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:25.008225Z","title":"Learning Representations for Automatic Colorization,","venue":null,"work_id":"443b5b60-5432-4119-a2f9-c41928da7bba","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.464802Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:9356a51d543e96ac498fb1924b1f68bcd15365b5ea2c62d254919d6608410579","observation_id":"08a35ef4-ccc4-4e6c-92ab-c1796a7ddad8","resolution":{"observed_at":"2026-08-10T14:22:25.011813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.998485Z","title":"Photo-realistic single image super-resolution using a generative adversarial network,","venue":null,"work_id":"5a49232d-dcc1-4d69-97cc-d8c864565c33","year":2017},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.468136Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:9fb6aac168257bc54ab8fc9e6c2b248530615ab123420ee3bbba4cf2c7e7030c","observation_id":"3b060291-1bf1-460e-a921-38c8adb40e89","resolution":{"observed_at":"2026-08-10T14:22:25.001947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.988740Z","title":"Context encoders: Feature learning by inpainting,","venue":null,"work_id":"5105116f-fc6a-4634-afe1-8da4e287ae3c","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.471371Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:5edfe51dd1b740a0043651b449db742be3058af9387ad170b0ebbf3586445fe2","observation_id":"844ef733-f1db-4fcc-bb34-83cce4018cf7","resolution":{"observed_at":"2026-08-10T14:22:24.992176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07728","last_updated":"2018-03-21T03:21:14Z","snapshot_observed_at":"2026-08-10T04:03:30.714422Z","submitted_at":"2018-03-21T03:21:14Z","title":"Unsupervised Representation Learning by Predicting Image Rotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07728","snapshot_observed_at":"2026-08-10T14:22:24.474824Z","title":"Unsupervised Repre- sentation Learning by Predicting Image Rotations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.474824Z"},"links":{"cited_paper":"/paper/1803.07728","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:207f96ac8bf8075888668d83720d4e8cd04cb4a038b0dcc5f02ae2965c97f0fe","observation_id":"59706051-b913-4c4b-b010-2deab4636d74","resolution":{"observed_at":"2026-08-10T14:22:24.474824Z","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":"2026-08-10T14:22:24.978788Z","title":"Unsupervised Visual Repre- sentation Learning by Context Prediction,","venue":null,"work_id":"d7d0754d-ca73-4161-8244-f0c7448636d3","year":2015},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.478635Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:0e15f963d1dc5a86ade8e468c3c2e14ca2bf0fb518bbe825267cddba73ff86e2","observation_id":"6cf01e70-5354-4bbb-bcb6-c91bd678a685","resolution":{"observed_at":"2026-08-10T14:22:24.982402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.969133Z","title":"Split-brain Autoencoders: Unsu- pervised Learning by Cross-channel Prediction,","venue":null,"work_id":"8a245ee4-0dbc-4028-8fb4-7298fa8d78ea","year":2017},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.481992Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:1da15f29c689bb85599c4efda5ca35a3b4dfc0756c4da08ebfede7611a96d39b","observation_id":"6ae31582-72da-4e86-a2cc-e145ffd9b756","resolution":{"observed_at":"2026-08-10T14:22:24.972328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.960367Z","title":"Deep Clustering for Unsupervised Learning of Visual Features,","venue":null,"work_id":"ea9d3ad7-9aba-4fc5-b873-0b2fd69da046","year":2018},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.485213Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:1060dd5dbe9f3acd58aaf15bc21d6a6a64ad4e5bdd4bc67eeed89ae1984964f6","observation_id":"5fea129c-91c5-4757-aa9d-a831286a96ee","resolution":{"observed_at":"2026-08-10T14:22:24.963211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.05371","last_updated":"2020-02-19T18:03:39Z","snapshot_observed_at":"2026-08-10T09:53:58.049284Z","submitted_at":"2019-11-13T09:47:49Z","title":"Self-labelling via simultaneous clustering and representation learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.05371","snapshot_observed_at":"2026-08-10T14:22:24.488379Z","title":"Self-labelling via Si- multaneous Clustering and Representation Learning,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.488379Z"},"links":{"cited_paper":"/paper/1911.05371","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:a90bee7dde4afd604afaa45ca8da718ecbaa7e8ab2a2c17421449bd2b7d9bbd9","observation_id":"30134ba5-5662-4520-b4cd-1bd488eab7aa","resolution":{"observed_at":"2026-08-10T14:22:24.488379Z","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":"2026-08-10T14:22:24.951076Z","title":"Obow: Online bag-of-visual-words generation for self-supervised learn- ing,","venue":null,"work_id":"17637d62-86b1-451d-9de7-520ff99ec0a2","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.493163Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:8af42817a2bcd834462e18fc1f7e0849e6ccb43ed4a213c0ff37fe3d9ba519ce","observation_id":"678d8525-a1ef-487e-8b21-e17fd3d577e2","resolution":{"observed_at":"2026-08-10T14:22:24.954632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.942529Z","title":"Exploring Simple Siamese Representation Learn- ing,","venue":null,"work_id":"4575e153-da62-4dd6-a9ac-e8d80199faf1","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.496225Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:87a35d460e66145edba05f422d1d12f6561549de1c8cb28e48c0e0dd0fae1428","observation_id":"b040d653-02a5-471d-b618-6fb34184bb5a","resolution":{"observed_at":"2026-08-10T14:22:24.945451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.934674Z","title":"Emerging properties in self-supervised vision transformers,","venue":null,"work_id":"54378eaa-399f-44fb-839f-0a80c7944619","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.499631Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:55d05b0ca47c5e44d9214990e70b330916f2953ddb203e9a2aaf2381ac34ec03","observation_id":"2f363758-ee6b-49ef-a6fe-991f3caca399","resolution":{"observed_at":"2026-08-10T14:22:24.937467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.926341Z","title":"Billion-scale similarity search with gpus,","venue":null,"work_id":"85976a0c-d1da-413d-a4a7-a8528c98088d","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.503400Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:b35578bf4d5b1f847b4baf28484d4730fc983445b4a14df7420a442bfd06379b","observation_id":"ee331faa-9398-4aff-8646-9aa3aec8cc72","resolution":{"observed_at":"2026-08-10T14:22:24.929159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-10T14:22:24.507178Z","title":"Representation Learning with Contrastive Predictive Coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.507178Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:acf75e12e78ee844b91b6ee163cb9c0d3b2c24b712e53aebf55969566d921b39","observation_id":"19c74b7f-331e-41fc-8685-e13cc2ca445b","resolution":{"observed_at":"2026-08-10T14:22:24.507178Z","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":"2026-08-10T14:22:24.917766Z","title":"Learning Representations by Predicting Bags of Visual Words,","venue":null,"work_id":"150f2ecb-c7ee-4ba1-94b3-dbc338b36941","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.511242Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:a9e9fa0ba176a929727fb9420a978e952b2155c7a8c2370a1030a67adf0692c2","observation_id":"7016ec27-2053-4de1-9d08-67f3264a93ab","resolution":{"observed_at":"2026-08-10T14:22:24.920788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.908146Z","title":"Gradient-Based Learning Applied To Document Recognition,","venue":null,"work_id":"a131db1d-dc96-46ee-8bea-11565d8f54d9","year":1998},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.514554Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:d73e5cc654de51596015d4e038ed0202bb31cf9c5b40263e8ff699f3988ee74b","observation_id":"aadc3aa0-81cc-4888-bd9c-d17f78926f5f","resolution":{"observed_at":"2026-08-10T14:22:24.911735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.897876Z","title":"Microsoft Coco: Common Objects In Con- text,","venue":null,"work_id":"10fcc347-6915-41fe-93c1-fb4c0f2ec31f","year":2014},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.517726Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:a0217a6260495b25123159985cbca6e8bbc5b1a776de9278e9eba85779bcd287","observation_id":"26700d4d-e9ea-47f3-a7e6-baf502938206","resolution":{"observed_at":"2026-08-10T14:22:24.901558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04867","last_updated":"2020-02-21T13:36:15Z","snapshot_observed_at":"2026-08-09T06:48:42.729935Z","submitted_at":"2019-10-01T17:06:29Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.04867","snapshot_observed_at":"2026-08-10T14:22:24.520964Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.520964Z"},"links":{"cited_paper":"/paper/1910.04867","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:1cff8d26aa960ab53891880141a8ee4d09ef5f2bf7b2b61e0abb734c8c223c22","observation_id":"6e4aacdb-aeb4-4fb5-94d8-cd4ae8af75d8","resolution":{"observed_at":"2026-08-10T14:22:24.520964Z","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":"2026-08-10T14:22:24.888514Z","title":"Scaling and Benchmark- ing Self-supervised Visual Representation Learning,","venue":null,"work_id":"bb76686a-8279-4d30-a423-225117f47327","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.524625Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:76762c67cde1fbc71c0d32e8331ba8a8ad1982861397211114f1ada3aa82a352","observation_id":"62839781-a121-4227-b363-3528ca0aa085","resolution":{"observed_at":"2026-08-10T14:22:24.892063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.878734Z","title":"Do Better Imagenet Models Transfer Better?,","venue":null,"work_id":"6a109520-53d1-460b-b63a-39a8de84a52f","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.528152Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:9d99fbc46bb98b85a1cf7b89e6db43da00889c303d0d729989ba5ea451c06e9e","observation_id":"2af946fa-17fe-4d59-9d16-167639c75ddc","resolution":{"observed_at":"2026-08-10T14:22:24.882473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.869779Z","title":"How Well Do Self- supervised Models Transfer?,","venue":null,"work_id":"0a8cc0c4-9f87-49a9-88d5-0c70b8914790","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.532090Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:b48250db56b216bc76c05a42f2bf653bcbdac191d3eebce9e96c4742a0c72f1b","observation_id":"49275b55-4149-4d90-a7d6-3e108adbaab4","resolution":{"observed_at":"2026-08-10T14:22:24.872980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.860522Z","title":"Natural Adversarial Examples,","venue":null,"work_id":"48136cc5-1ab1-4b62-a25c-47d7f0cdd907","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.534986Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:4831aeb3bd563b4ef9f745f4132bfa838ee5b28807ef130d24c98ccac7ed8918","observation_id":"a5b2af5a-c66b-4694-b996-8c791f715a76","resolution":{"observed_at":"2026-08-10T14:22:24.863589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.850853Z","title":"Learning Robust Global Representations by Penalizing Local Predictive Power,","venue":null,"work_id":"79347230-8eaf-4af6-90a3-d9e1596a4438","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.537621Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:6e073d3695fd87bcb80552c32c89db8e9cdb49550574eec136f615f3b75d38de","observation_id":"284a6010-79e4-4205-9e16-8fe42df9855a","resolution":{"observed_at":"2026-08-10T14:22:24.854172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.841350Z","title":"The many faces of robustness: A critical analysis of out-of-distribution generalization,","venue":null,"work_id":"006165cb-a176-4062-9dc1-3a94a8336c1e","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.541134Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:277fd3cc4375ac0b1b013061b2bb9880fdd45636ee311afd02143c5a13b76d73","observation_id":"90327375-6363-43b4-bf53-0e620397d9f6","resolution":{"observed_at":"2026-08-10T14:22:24.844540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.832568Z","title":"Improving Robustness Against Common Corruptions by Covariate Shift Adaptation,","venue":null,"work_id":"56b19952-c2b3-4337-abe4-3c2101d075cf","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.543835Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:f4cbd40bdd8e564c5e7485f6af35d876da623b5d9652e2e1531c717d2dce4d8d","observation_id":"a2828057-219a-4408-93ae-e136c0e6041e","resolution":{"observed_at":"2026-08-10T14:22:24.835577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.823119Z","title":"Do imagenet classifiers generalize to imagenet?,","venue":null,"work_id":"6e44a1cd-23a0-4f0f-ad08-7c28ed72cde6","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.547313Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:261f943b357ba8c21bdc5f60352042879d376c83abd04a1f8ee33a1ebccdb4ef","observation_id":"3d2abb71-32a1-4644-acbd-0c48a883502a","resolution":{"observed_at":"2026-08-10T14:22:24.826738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.813897Z","title":"Impact of ImageNet Model Selection on Domain Adaptation,","venue":null,"work_id":"2cac8e11-ffc3-4b60-98f8-f31f9765b810","year":2020},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.550189Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:c2cbf76ea2fbc84e24012f669f4db7d68146818afb7c078a7336dadcd79cc668","observation_id":"5ca3d186-de6e-4103-9f6f-2a2de5d21156","resolution":{"observed_at":"2026-08-10T14:22:24.817030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00417","last_updated":"2021-06-01T11:59:48Z","snapshot_observed_at":"2026-07-06T11:14:46.817047Z","submitted_at":"2021-06-01T11:59:48Z","title":"Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners","version":1},"cited_work":{"arxiv_id":"2106.00417","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.00417","snapshot_observed_at":"2026-08-10T14:22:24.674257Z","title":"Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners","venue":"cs.LG","work_id":"c1677a86-3e44-4c3e-972d-45b4d7aed1d2","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.552938Z"},"links":{"cited_paper":"/paper/2106.00417","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:3ee33bbf820d091b763c790f1deaf09e4e7c9ae202bad98f5451f356223b4729","observation_id":"e9afa9a9-3160-4da2-9032-80561a8f2993","resolution":{"observed_at":"2026-08-10T14:22:24.679843Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02136","last_updated":"2018-10-03T07:32:57Z","snapshot_observed_at":"2026-08-02T06:41:33.803920Z","submitted_at":"2016-10-07T04:06:01Z","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02136","snapshot_observed_at":"2026-08-10T14:22:24.556333Z","title":"A Baseline for Detecting Misclassified and Out-of-distribution Examples in Neural Networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.556333Z"},"links":{"cited_paper":"/paper/1610.02136","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:486cc8d1a1b2d6df8f1f8f50145be09633ba75d5a4da7f6d51e70bd75cd3bd87","observation_id":"3da79000-24a1-4e57-8989-00d5a4202d23","resolution":{"observed_at":"2026-08-10T14:22:24.556333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.05566","last_updated":"2020-07-10T18:40:37Z","snapshot_observed_at":"2026-08-05T18:39:07.775274Z","submitted_at":"2020-07-10T18:40:37Z","title":"Contrastive Training for Improved Out-of-Distribution Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.05566","snapshot_observed_at":"2026-08-10T14:22:24.560028Z","title":"Contrastive Training for Improved Out-of-distribution Detection,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.560028Z"},"links":{"cited_paper":"/paper/2007.05566","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:4aea4b2313c3545ef793c28c12a08d4ee87f8ed8631f083a2314aaf1e0562d1f","observation_id":"2640b02a-4d70-49b0-9977-7f88757d6abf","resolution":{"observed_at":"2026-08-10T14:22:24.560028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11056","last_updated":"2021-11-22T08:54:34Z","snapshot_observed_at":"2026-08-10T16:24:46.575483Z","submitted_at":"2021-11-22T08:54:34Z","title":"Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.11056","snapshot_observed_at":"2026-08-10T14:22:24.563332Z","title":"Evaluating adversarial attacks on imagenet: A reality check on misclassification classes,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.563332Z"},"links":{"cited_paper":"/paper/2111.11056","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:e30df2d801ec32eb8e68b4d169fbbfc69f4f17cc896a8e51e1cc74577fe68662","observation_id":"04db514c-afe5-41d8-bc98-8d0a787cf627","resolution":{"observed_at":"2026-08-10T14:22:24.563332Z","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":"2026-08-10T14:22:24.805085Z","title":"Deep Residual Learning For Image Recognition,","venue":null,"work_id":"f223d001-f77e-41d2-b876-713508d48a2f","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.566320Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:94b1cac10a49208af20a5dcc3f5ec9b24d886dc41b21fbc1ee977b5ad1e43d50","observation_id":"28391a1e-2d75-48fc-bee6-ab7d2cf5dade","resolution":{"observed_at":"2026-08-10T14:22:24.808515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.796711Z","title":"Going Deeper With Convolutions,","venue":null,"work_id":"06d41de6-c156-4422-a06e-085e1bf79976","year":2015},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.569222Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:6994eb760b3bccb0f777d5fc18135f9da56d1fc5eb434dfec3f0fd54b941ab8f","observation_id":"c563558b-d8c7-4588-9eb8-ec3f1e1ec2f9","resolution":{"observed_at":"2026-08-10T14:22:24.799731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.787923Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels,","venue":null,"work_id":"d01c456c-f0fc-4e50-8cde-1835994bddfa","year":2021},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.572200Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:0286eac4747f0948b2c1037a28e0c4fa3b5ce20898fa8ecbff0c538e7593cef3","observation_id":"b9a7c5a9-c0ea-4768-ba3d-1b75b5eceb1e","resolution":{"observed_at":"2026-08-10T14:22:24.790745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.777908Z","title":"Selective brain damage: Measuring the disparate impact of model pruning,","venue":null,"work_id":"faf6fa5a-4592-463a-ada8-449e9b6e9088","year":2019},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.574939Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:2d5cd87a9c9be57dcc90c87e0a0932dbeb2692a120651c6ea33d0436350f31ed","observation_id":"b09017b6-7fb5-4221-8773-d985f19eac66","resolution":{"observed_at":"2026-08-10T14:22:24.781572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07159","last_updated":"2020-06-12T13:17:25Z","snapshot_observed_at":"2026-08-07T08:01:08.867333Z","submitted_at":"2020-06-12T13:17:25Z","title":"Are we done with ImageNet?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07159","snapshot_observed_at":"2026-08-10T14:22:24.579251Z","title":"Are We Done with Imagenet?,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.579251Z"},"links":{"cited_paper":"/paper/2006.07159","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:02ddaeeab22cdc01c4785ab2c11034b00abe14a9be0deae79396923940277101","observation_id":"14721a92-0cfb-40d9-bb65-71a8069a5139","resolution":{"observed_at":"2026-08-10T14:22:24.579251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.12231","last_updated":"2022-11-09T23:15:15Z","snapshot_observed_at":"2026-08-02T03:51:09.933624Z","submitted_at":"2018-11-29T15:04:05Z","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.12231","snapshot_observed_at":"2026-08-10T14:22:24.584079Z","title":"ImageNet-trained CNNs are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.584079Z"},"links":{"cited_paper":"/paper/1811.12231","citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:49347e3333b2c303fa1ca901a94f4399d27cad4eafd2333702adc488e7897e50","observation_id":"b26cf701-2921-4886-a62f-03e908f2c1de","resolution":{"observed_at":"2026-08-10T14:22:24.584079Z","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":"2026-08-10T14:22:24.767276Z","title":"Explaining and Harnessing Adversarial Examples,","venue":null,"work_id":"fcc1fa2b-3a2b-4fbb-992f-73502051705e","year":2015},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.588422Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:5447e6020ea028743f5c632d9ce27b86f668a61301c5d28a3a09830654e0e8fc","observation_id":"01b83b36-89fa-4b80-ad56-349491681eee","resolution":{"observed_at":"2026-08-10T14:22:24.770957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.756389Z","title":"Adversarial Examples In The Physical World,","venue":null,"work_id":"4f980a7b-6951-4dc1-ba68-79a03bdf5b59","year":2016},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.591759Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:d092850bf4aeedb4a1443d51c96b6933976163fb2810bde5097268998322ec97","observation_id":"b306310c-5f93-4cca-826a-a5b1d3f1cd9c","resolution":{"observed_at":"2026-08-10T14:22:24.760425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T14:22:24.746109Z","title":"Borenstein, L","venue":null,"work_id":"1e2d957f-1231-4040-b88d-14c74568c7a0","year":2009},"citing_paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T14:22:24.594618Z"},"links":{"citing_paper":"/paper/2501.15431"},"observation_digest":"sha256:129cea3d534876edb10ac1cc69ac1b227811b4af9b9b96e5a778078c91c55a07","observation_id":"eac66f4c-4d6a-4269-8828-65adc70e80eb","resolution":{"observed_at":"2026-08-10T14:22:24.749584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.15431","last_updated":"2025-01-26T07:19:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T20:13:31.343560Z","submitted_at":"2025-01-26T07:19:12Z","title":"Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":50},"total_outbound_references":63},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2501.15431."}