{"as_of":"2026-08-18T17:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5fa15bd2fd6ed78463e0c5f86f97eb100c58e897cfeb9330168521bf5b681de6","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:57:25.874756Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2508.19581/citation-record","integrity":"/paper/2508.19581/integrity","json":"/paper/2508.19581/citation-record.json","paper":"/paper/2508.19581"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:57:25.637102Z","title":"Reverse-time diffusion equation mod- els","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.637102Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:4d8396578b9318fb047eb72fe196066f4b6f314f8f4dc4d0a438c761725be644","observation_id":"7932e332-4f35-4a30-bea9-e7b167231423","resolution":{"observed_at":"2026-08-15T16:57:25.637102Z","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":"2026-08-15T16:57:26.546487Z","title":"From noisy predic- tion to true label: Noisy prediction calibration via generative model","venue":null,"work_id":"6f98df63-3b25-49d9-b137-dd34982be565","year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.641139Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:4160807f372f7d2cd04d29e1bbb5666c9d09dd9c17530d298643a45fa735aa4a","observation_id":"31256320-3e8e-49ea-bee0-97ea075cf206","resolution":{"observed_at":"2026-08-15T16:57:26.550421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.535351Z","title":"Understand- ing and improving early stopping for learning with noisy la- bels","venue":null,"work_id":"102091f6-13a2-4912-a3c3-10c434ce0f01","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.644590Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:5548e283cf81a538c259be4286a987b6057aa7bfff98cba09b7c1b370a95e544","observation_id":"48af4903-274e-456d-b687-127892d12a80","resolution":{"observed_at":"2026-08-15T16:57:26.538884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.648187Z","title":"Frozen in time: A joint video and image encoder for end-to-end retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.648187Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:ab152653016aa75eb9cda03b0c667cdef9d4f380e7f420239f559f81a8b0c3e4","observation_id":"f9c7cb0f-95c0-4a19-95c9-3bcafbebeb39","resolution":{"observed_at":"2026-08-15T16:57:25.648187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01973","last_updated":"2018-06-21T14:54:33Z","snapshot_observed_at":"2026-08-15T02:02:54.527877Z","submitted_at":"2018-01-06T05:44:29Z","title":"A Note on the Inception Score","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01973","snapshot_observed_at":"2026-08-15T16:57:25.651874Z","title":"A note on the inception score","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.651874Z"},"links":{"cited_paper":"/paper/1801.01973","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9f53f04f0375cc83c588bf8f43c958ab07beb8d98561e71864faba93504b928e","observation_id":"f6684826-91bd-4c17-a4b2-2114bdfd2726","resolution":{"observed_at":"2026-08-15T16:57:25.651874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07159","last_updated":"2020-06-12T13:17:25Z","snapshot_observed_at":"2026-08-15T10:22:52.364884Z","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":"2026-08-15T16:57:25.655810Z","title":"Are we done with imagenet? arXiv preprint arXiv:2006.07159, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.655810Z"},"links":{"cited_paper":"/paper/2006.07159","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:aff58434851b1fd064f26f164e626e92a1b1ba89fd4097e72819c99cd4312a11","observation_id":"ccfdb4d1-f2b2-4285-8155-5d02e1634476","resolution":{"observed_at":"2026-08-15T16:57:25.655810Z","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":"2026-08-15T16:57:26.518187Z","title":"Food-101–mining discriminative components with random forests","venue":null,"work_id":"c57ab845-0e64-471e-a600-abcd45215066","year":2014},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.659939Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:d0e80b59e7eda05e98f190746bd355615e7b2cd0fd4cbab728a6a67a4fb6603c","observation_id":"ecfb8d59-adcb-4cb1-a294-5cab63d6954c","resolution":{"observed_at":"2026-08-15T16:57:26.522094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.506896Z","title":"Denoising likelihood score match- ing for conditional score-based data generation","venue":null,"work_id":"7586a876-811c-433a-850e-91fbc6bb02ee","year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.663720Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:4acb4efe023e3547384b2c03fe11a37fb9de5c1e0409cfda0ee5be2a20186a7f","observation_id":"b02c03eb-c91a-416b-8a11-6f22ceb616f8","resolution":{"observed_at":"2026-08-15T16:57:26.510549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.495938Z","title":"Slight corruption in pre-training data makes better dif- fusion models","venue":null,"work_id":"06c38dbc-5280-4d10-ab64-e602d68b57e5","year":2024},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.667145Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:cd3b4dc4e7e933f989a86c89d362600dfb5043551d88df42724b52f9cdfccea5","observation_id":"7af612d7-21e2-48cc-804c-ffe91ba98481","resolution":{"observed_at":"2026-08-15T16:57:26.499584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.670893Z","title":"Label-retrieval- augmented diffusion models for learning from noisy labels","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.670893Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:20227de09808097c7299be35ab8a330d910efcd59c43bf1fc6d3d8d11f742fb5","observation_id":"9e83f5f1-8248-48f3-88b2-c43300408b57","resolution":{"observed_at":"2026-08-15T16:57:25.670893Z","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":"2026-08-15T16:57:26.478890Z","title":"Learning with instance-dependent label noise: A sample sieve approach, 2021","venue":null,"work_id":"4ace34d9-e035-4e6c-8661-d2adefe2abf1","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.674571Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9185a2c99e9c02706baa1160af12d69b805383b1fc095cdba5e74f73fca6c064","observation_id":"c238f85b-855b-4872-8924-380fcda9e24c","resolution":{"observed_at":"2026-08-15T16:57:26.482499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.09022","last_updated":"2022-05-26T09:58:47Z","snapshot_observed_at":"2026-08-18T10:07:15.584218Z","submitted_at":"2021-10-18T05:41:57Z","title":"Mitigating Memorization of Noisy Labels via Regularization between Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.09022","snapshot_observed_at":"2026-08-15T16:57:25.678375Z","title":"Mitigat- ing memorization of noisy labels via regularization between representations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.678375Z"},"links":{"cited_paper":"/paper/2110.09022","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9edc3a481feee24365bbdc88989458f9401b9a5d5dbda7d748b50c9fcc7a8a5e","observation_id":"2cdd10d8-b597-4eb1-b3ef-829df7c52d4b","resolution":{"observed_at":"2026-08-15T16:57:25.678375Z","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":"2026-08-15T16:57:25.682307Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.682307Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9a07acb18a88f15d97e4ed96afc8873d60650f2c5f781a858fe8edf4c0db1aaa","observation_id":"f36ba01a-c46c-4ae7-9e5b-4abfb41a842e","resolution":{"observed_at":"2026-08-15T16:57:25.682307Z","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":"2026-08-15T16:57:25.685829Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.685829Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:a140aacee46d711099407d88e9e9c4b693828b50f8e423ffada27919fafe1f46","observation_id":"c4882d3f-6f36-4b7b-8caa-df12a68fb1be","resolution":{"observed_at":"2026-08-15T16:57:25.685829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.01412","last_updated":"2021-04-29T16:44:25Z","snapshot_observed_at":"2026-08-15T12:34:14.131778Z","submitted_at":"2020-10-03T19:02:10Z","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.01412","snapshot_observed_at":"2026-08-15T16:57:25.689478Z","title":"Sharpness-aware minimization for efficiently improving generalization","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.689478Z"},"links":{"cited_paper":"/paper/2010.01412","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:0335af8d7fa74d3521999e3f8c5a1e7f860a5485eb3aad641e40c5eccdbd2fc0","observation_id":"dcccb6b7-8418-490a-b44e-223209b7e6a0","resolution":{"observed_at":"2026-08-15T16:57:25.689478Z","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":"2026-08-15T16:57:26.456432Z","title":"Generative adversarial nets","venue":null,"work_id":"b20a2624-e6c0-43cd-b31a-6962fae61f80","year":2014},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.693281Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:fb6759182c9371674581944d63268f70ba0532f8b2e0deaa183ae13145ea3026","observation_id":"eaf431c7-570b-42c5-88a0-8ee4a6aee97b","resolution":{"observed_at":"2026-08-15T16:57:26.459551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.445690Z","title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","venue":null,"work_id":"254fd8c8-4f8b-4790-b316-2b5a411be08c","year":2010},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.696562Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:65b59b178174d23fd03c085912dc3f63a9c4c7080b909ddc1570cf03cab88cf7","observation_id":"785459ef-6237-4945-98bb-d982f0a6061d","resolution":{"observed_at":"2026-08-15T16:57:26.449373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.434815Z","title":"Co- teaching: Robust training of deep neural networks with ex- tremely noisy labels","venue":null,"work_id":"6fb98c8e-fd6e-477f-bb16-e3c2d5e7be18","year":2018},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.700076Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:16a871c843bff5a0f887247062a418d515f5972046bb60e3386c53ced0bd55fe","observation_id":"6d5b9213-c997-4e63-906c-d923bfbd3efa","resolution":{"observed_at":"2026-08-15T16:57:26.438426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.423798Z","title":"Improving generalization by controlling label- noise information in neural network weights","venue":null,"work_id":"66017071-db82-4b10-ac43-f662593141e4","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.703452Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:0336fe07ec8ba892bec9561050aeed3c2c8b51092ac1699ca9e494ca6f3ec4e2","observation_id":"3b9ea062-63dc-4b97-a2ae-285601494d3b","resolution":{"observed_at":"2026-08-15T16:57:26.427454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.707041Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilib- rium","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.707041Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:3eddf0e4245832bb2a7614def89b5de4a0122d34108d1cda5b8c4df15cba4c51","observation_id":"544cab6b-3839-4d31-a620-76803ed491ce","resolution":{"observed_at":"2026-08-15T16:57:25.707041Z","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":"2026-08-15T16:57:25.710623Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.710623Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:a6e00e3e4af596ceef5a1d7135ec226b60a405c9f4d4d161d9c5dfe124ffe0f3","observation_id":"58bdf165-6d94-4e71-96bb-e4258a5dec22","resolution":{"observed_at":"2026-08-15T16:57:25.710623Z","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":"2026-08-15T16:57:26.400575Z","title":"Label-noise robust generative adversarial networks","venue":null,"work_id":"df1cc732-9efb-45f7-b1ad-e59cda4dfd53","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.714249Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:e05ae24dfa17c7f5fa39f3d0476d21565b41d9b4e515a4c4c345ad66e9013532","observation_id":"1b468f0c-bc0f-4b57-a11f-91e74d69daee","resolution":{"observed_at":"2026-08-15T16:57:26.404226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.718097Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.718097Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:44073faa5b680fa2eb52e8abcb259fc2fd7fcb201ec5f586ac8b0896a9afb965","observation_id":"5a7c8b4c-494a-4f73-9ba6-fcd81864c1e9","resolution":{"observed_at":"2026-08-15T16:57:25.718097Z","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":"2026-08-15T16:57:26.383342Z","title":"Refining generative process with discriminator guidance in score-based diffusion mod- els","venue":null,"work_id":"0527eeca-e4c8-462d-a455-26169e9e0b14","year":2023},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.721515Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:d248e88cfffdbb0b8a1c52a956db14385224b86da55279f4eae2790e204a4abb","observation_id":"61c5c67a-b308-453c-9096-cd360ab14c1a","resolution":{"observed_at":"2026-08-15T16:57:26.386925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.372888Z","title":"Learning discriminative dynamics with label corruption for noisy label detection","venue":null,"work_id":"69cb2d0a-40b0-4b9c-846c-9cc5ecc2acde","year":2024},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.725097Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:d9f4f81831a7b94d5f8a021a3851225d6f0cbf4265347b51d0cd8919ff0adea2","observation_id":"681cb76b-b6ff-48a7-8dc4-16a965e421c8","resolution":{"observed_at":"2026-08-15T16:57:26.376603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.362185Z","title":"Nlnl: Negative learning for noisy labels","venue":null,"work_id":"f9e8a7db-e136-477d-8901-0faab164ca93","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.728610Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:f03b3bd4f94f9e2a2b806336e50bd4025ea4b49da621fb4f7f638c8245db160e","observation_id":"c497e7d2-d595-4377-807d-f63a6e17fb60","resolution":{"observed_at":"2026-08-15T16:57:26.365836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.351412Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"378a6414-48cc-44b2-9171-93d10e57a6cb","year":2009},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.732137Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:27de47b0acd0d673959060bf44aa6c0692822d24ff882946faad460d7e7d9ea3","observation_id":"f3992461-a878-422e-974d-c1041b803325","resolution":{"observed_at":"2026-08-15T16:57:26.355466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.339108Z","title":"Learning with noisy labels by efficient transition ma- trix estimation to combat label miscorrection","venue":null,"work_id":"2c0d0a27-1aaa-446d-85d2-386861e6dfda","year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.735433Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:27b22e450b05df4f62e7eeef9439c9572c5993bafdaf97e92300619e4bf15296","observation_id":"bdc59720-3813-4d16-9a9a-ca53c01143ac","resolution":{"observed_at":"2026-08-15T16:57:26.343864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07724","last_updated":"2024-11-06T14:29:36Z","snapshot_observed_at":"2026-08-16T14:01:46.140668Z","submitted_at":"2024-04-11T13:16:47Z","title":"Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07724","snapshot_observed_at":"2026-08-15T16:57:25.739008Z","title":"Applying guidance in a limited interval improves sample and distribution quality in diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.739008Z"},"links":{"cited_paper":"/paper/2404.07724","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:00fe0b7f699b5c15573c824320b415deaab4222afa23314b6d194a8c007c85b8","observation_id":"b9cda7d3-9f11-4a86-8502-d14a5693b91a","resolution":{"observed_at":"2026-08-15T16:57:25.739008Z","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":"2026-08-15T16:57:25.742640Z","title":"Tiny imagenet visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.742640Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:e7a1e8b20113df6fdd1dd0aded311e9902faa0ba099e0c6b27da1c99a8e5daca","observation_id":"7e5b4892-fe93-45d8-a134-1ad8f7206808","resolution":{"observed_at":"2026-08-15T16:57:25.742640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.09792","last_updated":"2020-11-11T09:59:16Z","snapshot_observed_at":"2026-07-06T08:31:15.005367Z","submitted_at":"2019-10-22T06:58:10Z","title":"Robust Training with Ensemble Consensus","version":3},"cited_work":{"arxiv_id":"1910.09792","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.09792","snapshot_observed_at":"2026-08-15T16:57:25.959486Z","title":"Robust Training with Ensemble Consensus","venue":"cs.LG","work_id":"c7ab1b4f-4ba5-40f4-84ab-91b75b0c613e","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.746382Z"},"links":{"cited_paper":"/paper/1910.09792","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:d1236fcf1d3b3c4f938d4f6a963bf1c261c7a91755d96162cece87725cf9be30","observation_id":"fad7d6f7-68f3-486f-9448-b4165485c297","resolution":{"observed_at":"2026-08-15T16:57:25.965394Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.320772Z","title":"Neighbor- hood collective estimation for noisy label identification and correction","venue":null,"work_id":"4169ce0f-d422-4f83-b31f-8d065a6e76e8","year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.750215Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:1957cdfd2b527b995e1fe5656c242fba89f82026fefab59d6467aec9873f014a","observation_id":"fbfbed6d-7977-43d8-bf5c-82f649fea38e","resolution":{"observed_at":"2026-08-15T16:57:26.324546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.308264Z","title":"Early-learning regularization pre- vents memorization of noisy labels","venue":null,"work_id":"aaaf673b-4c65-49d8-90d1-6e90cdb4ad0c","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.754347Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:0a713a08ae623a255b13c73c6b420e4c8919e0eae358144a85bda3b6063b2ca6","observation_id":"fd44de83-e542-4812-8f81-b4510602e2be","resolution":{"observed_at":"2026-08-15T16:57:26.312637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.296836Z","title":"Label- noise robust diffusion models","venue":null,"work_id":"b06531d0-41a6-4fac-be35-eda78cf8adad","year":2024},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.758000Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:125dcf3a2390a4800debd3eb0cd7013effca7fc17e82a972c8352d3ba32b465f","observation_id":"45220469-45ba-4f49-9977-38d4fdc0d469","resolution":{"observed_at":"2026-08-15T16:57:26.300332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.286029Z","title":"Reliable fidelity and diversity metrics for generative models","venue":null,"work_id":"61a2f764-e2bb-447d-b8f0-6ada4a820fec","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.761483Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:00b6d4ee5c655eec52e81b41587999adbe558f06a4318b667a8cbfc64534d0e9","observation_id":"6b2ae70f-d331-419c-a555-547ed5b7c50b","resolution":{"observed_at":"2026-08-15T16:57:26.289823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.274823Z","title":"Confident learning: Estimating uncertainty in dataset labels","venue":null,"work_id":"3b52c60f-2cca-4512-8165-1a08bce880a2","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.765088Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:832f3c8937c94ea4d9c95dbbce54745ec9b808289dc284c3e32939d0875d585b","observation_id":"f98c976e-6ef4-49c6-9a73-c7b2dcd389fb","resolution":{"observed_at":"2026-08-15T16:57:26.278600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.263999Z","title":"Memorization in deep neural net- works: Does the loss function matter? In Pacific-Asia Con- ference on Knowledge Discovery and Data Mining , pages 131–142","venue":null,"work_id":"88c3fdbf-f123-4869-8d2d-f2372998ab5d","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.768712Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:c37eda4b9f0e93859dfd90b19c7714422678360d64d050eec454bdaedf2dbdcf","observation_id":"c96f2b20-500a-43c9-8e5e-0b27cebc98d9","resolution":{"observed_at":"2026-08-15T16:57:26.267862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.253148Z","title":"Identifying mislabeled data using the area under the margin ranking","venue":null,"work_id":"db6a1205-5167-4de6-af6f-1069db749860","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.772392Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9ffc9495c4b9916defc8d913f3ac3ebbfd703efd7094eedead668de9cde87e69","observation_id":"c1617b0c-e854-4e2a-8f3b-a3e1478b196a","resolution":{"observed_at":"2026-08-15T16:57:26.256857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.776441Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.776441Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:dd5f8ba3ae9c152736e4c510c9d581e29c03e55e44f1c5f477a1ab3b59b49a1a","observation_id":"f9b9eef9-3777-4dda-8615-e0b48fbe6aad","resolution":{"observed_at":"2026-08-15T16:57:25.776441Z","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":"2026-08-15T16:57:26.235311Z","title":"Classification accuracy score for conditional generative models","venue":null,"work_id":"40765f8a-b988-4772-947d-a3e68e3535e6","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.780185Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:0a2e5305b3c1af84913b5949f5ff7289cf76dcaea318b4e8254178536ca636d3","observation_id":"dca8fccb-76e9-4e4e-88b2-1e165235cd2a","resolution":{"observed_at":"2026-08-15T16:57:26.239137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.224012Z","title":"Improved techniques for training gans","venue":null,"work_id":"6f250be6-31c0-4302-b9d5-dfe9e4cc5902","year":2016},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.783773Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:40d870918f4cfcc2269b1a166973b966e285af7efadf511528f66b1b9f4e4c6b","observation_id":"2ebfdcf8-76e8-4920-83ec-d9ec737ff87e","resolution":{"observed_at":"2026-08-15T16:57:26.228117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.787571Z","title":"Laion-5b: An open large-scale dataset for training next generation image-text models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.787571Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:08d84112e3e32cf8ae572c8a4c81b8ac5e40779cbcbceb2d95a46167a740d4d1","observation_id":"4775c156-26bd-4381-934f-63a2269e2947","resolution":{"observed_at":"2026-08-15T16:57:25.787571Z","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":"2026-08-15T16:57:26.206418Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":"f30cc09d-0ec2-487c-9682-68902b8835c8","year":2015},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.791855Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:6b0690647db1a9c11fd19aa62c831a6d6be0b0dd3ad984defd17f83b36d0976d","observation_id":"6e5d085c-a2af-425b-a189-04cf2e56fae4","resolution":{"observed_at":"2026-08-15T16:57:26.210239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-15T16:57:25.795452Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.795452Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9e0de0943e321f3281336bda6b38bc2dec41f2ee361af3a3f7e2ff8b6ac3f36b","observation_id":"40a6b3b6-b3d8-4238-abac-72e0ae54656a","resolution":{"observed_at":"2026-08-15T16:57:25.795452Z","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":"2026-08-15T16:57:26.195968Z","title":"Generative modeling by esti- mating gradients of the data distribution","venue":null,"work_id":"ed18df4d-b9fd-4da2-9542-d96c20cb3ee1","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.799250Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:f442d7f171ea90c779c91de40760aad1a6a78eab36897e4b6b6eae564d0be03b","observation_id":"67190ed6-e0c9-41b0-a0c0-87678e6dd665","resolution":{"observed_at":"2026-08-15T16:57:26.199410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.803809Z","title":"Score-based generative modeling through stochastic differential equa- tions","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.803809Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:b4616a09031f4e0a64ac36e9e2a5903b47d15503bf38fc64e618658de5844ddf","observation_id":"7e34c857-2c7a-4fe3-b0f0-9733de078bed","resolution":{"observed_at":"2026-08-15T16:57:25.803809Z","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":"2026-08-15T16:57:26.184876Z","title":"Robustness of conditional gans to noisy la- bels","venue":null,"work_id":"22e89095-d7e2-463b-94b2-f9efec182e27","year":2018},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.807795Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:368807a72703f01f6a367726cded8addfd9a4ba693ff9dec21ec9588c6508aca","observation_id":"c92a7dc9-af03-46ac-a6cd-e4490c016ed4","resolution":{"observed_at":"2026-08-15T16:57:26.188457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.173140Z","title":"Identifying and eliminating csam in genera- tive ml training data and models","venue":null,"work_id":"d1523578-9969-4693-83db-d77f568840c9","year":2023},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.811219Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9725ce00b2b4f5ce1bb064b7f95a5aec399faca765d88dfe72b0a5e6ad58eefe","observation_id":"d156c88f-781d-4df1-b561-d23f2fe5aac0","resolution":{"observed_at":"2026-08-15T16:57:26.176798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:25.814812Z","title":"A connection between score matching and denoising autoencoders","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.814812Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:acea75eec582ea6084e7ab90a0b2579aa9690778940e48bfd165568cc8db0577","observation_id":"c3c991aa-3d85-4ac0-8696-06002b672a65","resolution":{"observed_at":"2026-08-15T16:57:25.814812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14896","last_updated":"2023-07-06T11:53:19Z","snapshot_observed_at":"2026-08-16T16:20:53.427900Z","submitted_at":"2022-10-26T17:54:20Z","title":"DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.14896","snapshot_observed_at":"2026-08-15T16:57:25.818511Z","title":"Diffu- siondb: A large-scale prompt gallery dataset for text-to- image generative models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.818511Z"},"links":{"cited_paper":"/paper/2210.14896","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:03e533701a6d7e6dc8dc3b0f4122747895b54a815f30348ccd8ddf0d0ef595b5","observation_id":"5d85a29a-5fa7-40a5-a6cc-f1ad550b8538","resolution":{"observed_at":"2026-08-15T16:57:25.818511Z","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":"2026-08-15T16:57:26.156045Z","title":"Robust early-learning: Hindering the memorization of noisy labels","venue":null,"work_id":"3560bccf-eec7-45f1-b15b-8e622616d703","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.822500Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:9028afe3168c72fbfee5887bb0993b82cc40a9dee430b37e8cc384d89ac23e94","observation_id":"48b60186-7a76-46e0-8617-9dfb7547a379","resolution":{"observed_at":"2026-08-15T16:57:26.159858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.144536Z","title":"Part-dependent label noise: Towards instance-dependent label noise","venue":null,"work_id":"4e781fad-79ff-468a-8871-4fa0b06bce87","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.826191Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:7bdcb4db254fed522e17795f4b2ab44653862d9558020a8fddbcdf51cb26d82d","observation_id":"07011f42-689a-4684-91fd-c5846c2d4a6c","resolution":{"observed_at":"2026-08-15T16:57:26.148647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00445","last_updated":"2021-06-01T12:53:53Z","snapshot_observed_at":"2026-08-16T18:20:34.622910Z","submitted_at":"2021-06-01T12:53:53Z","title":"Sample Selection with Uncertainty of Losses for Learning with Noisy Labels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00445","snapshot_observed_at":"2026-08-15T16:57:25.829948Z","title":"Sample selection with uncertainty of losses for learning with noisy labels","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.829948Z"},"links":{"cited_paper":"/paper/2106.00445","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:78a8355aee3f472320dadf95d6cfaa7b4bd4b668d3bf5677d6618c9c44c0f29c","observation_id":"44350125-8e48-4541-b866-ac1784026cb9","resolution":{"observed_at":"2026-08-15T16:57:25.829948Z","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":"2026-08-15T16:57:25.833795Z","title":"Learning from massive noisy labeled data for im- age classification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.833795Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:630c5a54581f51ea9896d37dc3258e6654b945c09d47d8279c3f306b16b56100","observation_id":"d0d44ec4-f084-485b-83db-1ffc12cf3c4c","resolution":{"observed_at":"2026-08-15T16:57:25.833795Z","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":"2026-08-15T16:57:26.126120Z","title":"Dual t: Reduc- ing estimation error for transition matrix in label-noise learn- ing","venue":null,"work_id":"629f269d-6970-4dbd-8897-783a704f63cd","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.838139Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:4affa0ec9198a0aea70672e6e70ca521470eae34c277cae5b4f13a790cfbfdd9","observation_id":"f58580ac-cd95-4743-85e1-49ea3685e133","resolution":{"observed_at":"2026-08-15T16:57:26.129979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.115024Z","title":"On learning contrastive representations for learning with noisy labels","venue":null,"work_id":"9026a276-ce8b-439d-b299-a5dfec48a055","year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.841955Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:37462a8cd05f8901f208047285c0470f872f29330b352df081b65f6dcfb793bd","observation_id":"8c51f9d8-3477-459c-b059-2ca138b76213","resolution":{"observed_at":"2026-08-15T16:57:26.118641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.103869Z","title":"How does disagreement help gener- alization against label corruption? In International confer- ence on machine learning, pages 7164–7173","venue":null,"work_id":"61ffa344-1bee-413e-97e2-e9b1e90e305f","year":2019},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.845494Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:bc3f22dc61d20562829a3f8c20a5f273d33270d90e37857e8bb8a60180f9140c","observation_id":"2e406368-30f0-42c9-b87c-f219702c2d34","resolution":{"observed_at":"2026-08-15T16:57:26.107987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.093348Z","title":"Understanding deep learning (still) requires rethinking generalization","venue":null,"work_id":"0f0b0b28-c960-4396-8632-50c58aea03b7","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.849271Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:c05795e778d1d61f4704a8c5505a651407892d14fb704d4cb96bcf3259c85579","observation_id":"ce519c61-3c09-4e8c-a454-e95367872269","resolution":{"observed_at":"2026-08-15T16:57:26.096967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-15T16:57:25.852618Z","title":"mixup: Beyond empirical risk minimiza- tion","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.852618Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:331f5eaeef65c1a5fbff7ddef8695e70bb8f9a28b48a13e0c6cd05c3fd46b555","observation_id":"d8cd3c7b-3447-445c-bb50-bccad8c7fcb0","resolution":{"observed_at":"2026-08-15T16:57:25.852618Z","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":"2026-08-15T16:57:26.082281Z","title":"Learning noise transition matrix from only noisy labels via total varia- tion regularization","venue":null,"work_id":"a1c088b0-10f4-4a4e-883d-bf96dfcbfcc9","year":2021},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.856236Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:950741f773017a3144c925e8d567f3f5ec4b52af8ef63e3e48567239a7c58f3b","observation_id":"b7a1fc30-76b5-4cdd-8464-3fa026cfd9e6","resolution":{"observed_at":"2026-08-15T16:57:26.086054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.071229Z","title":"Differentiable augmentation for data-efficient gan training","venue":null,"work_id":"3846df78-ccc9-4aad-8402-42c1441cb7c7","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.859642Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:0eea589ad8b3f12353de2493ea87a5499cb524720908f8f6fe8706b61a0713b9","observation_id":"15a94b8b-2e30-49ae-b622-55715868e371","resolution":{"observed_at":"2026-08-15T16:57:26.075051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.060328Z","title":"Robust cur- riculum learning: from clean label detection to noisy label self-correction","venue":null,"work_id":"0763e9ed-4454-4c8e-9810-48a1293d43dc","year":2020},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.863043Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:3ae48d415492b652a5bea51486362ac477c9185d0fa838b55b46d666db2c24be","observation_id":"3e68d148-5cfd-4e0d-94e5-e43a9c3c7eda","resolution":{"observed_at":"2026-08-15T16:57:26.064223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.049570Z","title":"Detecting cor- rupted labels without training a model to predict","venue":null,"work_id":"bea26ef4-39af-48a8-8de7-aeae6de27604","year":2022},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.866799Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:c18ceba86b1ac46752da5059ce3fe68094c7ff8bebfe6c73244b1ab22d85b719","observation_id":"f1f820e7-16e5-4c4e-9948-d943979e269f","resolution":{"observed_at":"2026-08-15T16:57:26.053336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.037564Z","title":"First, given the objective in Eq","venue":null,"work_id":"f06e39a0-3e4a-47fb-a53a-908d2ada895c","year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.870361Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:511d31d5b3b2b6965fafa56e537a1b5a2efa860719f480dfabbd24e77257916e","observation_id":"4259e448-ec91-4a16-91b0-18d70f6e18c5","resolution":{"observed_at":"2026-08-15T16:57:26.041360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T16:57:26.023693Z","title":"unconditional term","venue":null,"work_id":"dd2ca1ad-3dc1-4594-add7-1f931ab3d4e4","year":null},"citing_paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T16:57:25.874756Z"},"links":{"citing_paper":"/paper/2508.19581"},"observation_digest":"sha256:1b668cb5d5365ffa1e51d47456092aeb78b21b5cdd567a0c4993fa6d4babd9c9","observation_id":"eeca84cd-a7b0-40ad-a2c0-bb17e6d0a0fe","resolution":{"observed_at":"2026-08-15T16:57:26.028339Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.19581","last_updated":"2025-08-27T05:29:07Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T16:20:42.546284Z","submitted_at":"2025-08-27T05:29:07Z","title":"Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":1,"verified_fuzzy":40},"total_outbound_references":65},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2508.19581."}