{"as_of":"2026-08-08T04:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2016d2845ae23d24b87042e2b0e52588ce3ceac57f82985568e617062813d9a3","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:29:23.083180Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2505.24443/citation-record","integrity":"/paper/2505.24443/integrity","json":"/paper/2505.24443/citation-record.json","paper":"/paper/2505.24443"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:29:35.254767Z","title":"O’Connor, and Kevin McGuinness","venue":null,"work_id":"9b03aa5b-bb51-43f4-a054-e0c251124434","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.199952Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:1227fe5679cac9756932d2c941eb235cc4d7bed9e5119dc8e72dedfb32c0b5ef","observation_id":"70cff29c-b8cd-4ab9-9f07-11df694ed633","resolution":{"observed_at":"2026-08-07T12:29:35.373632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:34.958817Z","title":"Cubuk, Alex Kurakin, Ki- hyuk Sohn, Han Zhang, and Colin Raffel","venue":null,"work_id":"3af01c45-13d6-4b2c-8b00-3f83e1068244","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.261392Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:e734e888fca38bf4b7b33ae15f110000f558c589618da693692dc28a4abbe7e6","observation_id":"de7b9d08-babf-45a1-9735-6815e31eeaf8","resolution":{"observed_at":"2026-08-07T12:29:35.086085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:34.633325Z","title":"Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel","venue":null,"work_id":"1c9c47b9-4cb1-4fd4-9a84-51e36a101e3a","year":2019},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.342317Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:371a211b4c4813878ca48b32b1cc6898ec46a749fb84f7a1715f60b0bdfcbc4e","observation_id":"eb4786bf-cf2c-4efa-b907-89b899a16dd0","resolution":{"observed_at":"2026-08-07T12:29:34.794289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:34.365728Z","title":null,"venue":null,"work_id":"10d2f1b7-dfbd-402a-b9e9-cf026c9bca13","year":2010},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.437348Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:8607afd8066cede45044729e37f7b70022a04d0272e02f30ead7cbee38e77334","observation_id":"45ed394a-dbf3-4575-83f8-06efdec252be","resolution":{"observed_at":"2026-08-07T12:29:34.481118Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:34.044488Z","title":"Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning","venue":null,"work_id":"1f94c48f-7f03-4be1-98ea-4af7896f2887","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.538614Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:69db0a7dde203333ea5fda84e45ff072106219317bf328b673b370d6f4444d7b","observation_id":"eb8d902d-4b6a-4105-bacc-b14db67d4572","resolution":{"observed_at":"2026-08-07T12:29:34.219633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:33.769658Z","title":"Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning","venue":null,"work_id":"9b83ee5f-416d-47b6-8006-051e4c1fe3a4","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.624822Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:b5d7085449c6554f8d6ec3a5b4ffc209b300d7e5c6bd6648850b3cfb4f733ea2","observation_id":"158652e9-9eb7-46b1-be16-71291851f00f","resolution":{"observed_at":"2026-08-07T12:29:33.836172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:33.525252Z","title":null,"venue":null,"work_id":"bde94240-cc2a-4d2e-a2d1-4e762edbb1b4","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.718213Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:2a02f6a36ba45e17d06dcd21d4c9935b8acf2fd3b325fedc326243bda4216b2e","observation_id":"1606d608-6c70-4504-969c-15c557b9529a","resolution":{"observed_at":"2026-08-07T12:29:33.631853Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:33.285463Z","title":"Exploring simple siamese representation learning","venue":null,"work_id":"5a751bf3-feec-4079-b548-616fc34fa7bd","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.860854Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:d6bfba6ee391ee7633bc67b9638258491485b2fdac25af9502bf168ece823463","observation_id":"1d2fe95a-5dab-468a-a69a-a46cdec345c5","resolution":{"observed_at":"2026-08-07T12:29:33.374401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:33.015509Z","title":"Semi- supervised learning under class distribution mismatch","venue":null,"work_id":"a0011cfe-399a-493f-b4f1-6dc4bc4513d0","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:17.938854Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:d73367e867fcb31059f891e8fd2d6192f0750444a2e9a0cde73566fa5ed54d6b","observation_id":"b74711bf-5900-44fb-a99d-b2e88e3280ac","resolution":{"observed_at":"2026-08-07T12:29:33.192237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:32.834161Z","title":null,"venue":null,"work_id":"3757d836-7f7f-4f69-9668-a5a48d6fb88c","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.073668Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:0fe5dbb65e32a0fc2a97efe24e66ff7f89dc8f5496e878fff90317d401009be0","observation_id":"df920595-5d78-4fc2-8bad-e9a3d74bde70","resolution":{"observed_at":"2026-08-07T12:29:32.916234Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:32.649795Z","title":"The cityscapes dataset for semantic urban scene under- standing","venue":null,"work_id":"9cdb6351-df72-4a7e-8481-d812cd3c4d28","year":2016},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.190044Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:b035ebbd9ec953b0e4514f737c39bcbd31450d679bac79f7e4ace8137220c251","observation_id":"051ca930-addc-41e4-aa9a-94e9df9434b5","resolution":{"observed_at":"2026-08-07T12:29:32.715829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:32.478030Z","title":"Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V","venue":null,"work_id":"099832ad-fb83-403b-ae78-bded3a3979f5","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.355407Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:72d1d39ea7c6d15ad2ecf7870dcd5b7013ce78701d46acf82f71a6fbc7e4a78e","observation_id":"e2827b9e-648d-4b8b-828d-614f91af0583","resolution":{"observed_at":"2026-08-07T12:29:32.553220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:32.336977Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"0b40a417-be98-4300-9443-71b997c1adf2","year":2009},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.521625Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:4f29eacfe63dda79091164871e7e84a01737a4b0674c06fe7ae9c771e51b5d16","observation_id":"980529c2-83ae-4c74-a89a-6fbd01cd9902","resolution":{"observed_at":"2026-08-07T12:29:32.433723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:32.182201Z","title":"Semi- supervised learning via weight-aware distillation under class distribution mismatch","venue":null,"work_id":"01c92cc5-a186-4d09-8893-a2fdbd25b35f","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.702033Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:74dbdbecf7a46fe0a4292c8215bb1b85d0f6886d2f25863f774941e797e8c27d","observation_id":"519d912f-4493-4b64-8db0-1920325cb2bc","resolution":{"observed_at":"2026-08-07T12:29:32.267048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.947145Z","title":"Mutexmatch: Semi-supervised learning with mutex- based consistency regularization","venue":null,"work_id":"e4ae6040-c6e2-4645-918b-eb58e60a6d40","year":2024},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.804293Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:58aa3a344feddb07c44784c72de775ddfc2a889823c05fba060a23c6d506df71","observation_id":"4567f0ee-1cf3-4a66-a709-636d131bf263","resolution":{"observed_at":"2026-08-07T12:29:32.094338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.771613Z","title":"Ssb: Simple but strong baseline for boosting performance of open-set semi- supervised learning","venue":null,"work_id":"3186aed1-5a30-4df3-b681-a1d00221dfd3","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.912046Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:73e42dd1a3ab45949d4b1ca374658467cbb0a6825d06441c0a32c726d8ca6151","observation_id":"3b3af617-4130-41f1-92a1-5bb9c860dff1","resolution":{"observed_at":"2026-08-07T12:29:31.843459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.602371Z","title":"Semi-supervised learning by entropy minimization","venue":null,"work_id":"612de51f-23ce-4448-9e9c-9e682681a468","year":2004},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:18.984744Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:1ba352586b90e6cbf0b70296f8675ac3ff5b59f7970e00d613ac57767bb4267a","observation_id":"10659e81-ad1e-43d2-9a7e-5038f68b1e9e","resolution":{"observed_at":"2026-08-07T12:29:31.656408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.457328Z","title":"Safe deep semi-supervised learning for unseen-class unlabeled data","venue":null,"work_id":"8fbc80fc-b4e0-4e40-908f-c5e9071aa2c4","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.040799Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:5e19de217da90dc129d1d36b259fbfaa1a1633eda7fec4699e8b60bb5f7d1d8b","observation_id":"0e8eb46a-3f96-4b32-8b9b-aa9a86444c19","resolution":{"observed_at":"2026-08-07T12:29:31.526148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.305517Z","title":"Binary decomposition: A problem transformation perspective for open-set semi-supervised learning","venue":null,"work_id":"160d6a5f-970a-4267-b419-ec04671c3dc9","year":2024},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.099350Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:6aaf341f755e63968ed5d01f6773db3b9e42acb93afeb992e07a5605e2d5703d","observation_id":"e181c97c-1beb-4fd9-acf3-6d22352f71dd","resolution":{"observed_at":"2026-08-07T12:29:31.387208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:31.108701Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"3ed2277e-7b5b-4879-bc9b-59c638f685f5","year":2016},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.151694Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:ca9157dfdf10fe9bed50b4b7e7c059a1b49e473a0501fe773a7deed0636d36ba","observation_id":"9fc4bd72-31d6-4be9-af47-58d3ac9dcda4","resolution":{"observed_at":"2026-08-07T12:29:31.143974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:30.920095Z","title":"Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data","venue":null,"work_id":"a4b27ffe-914b-4f11-89c5-2b3cb3b5a571","year":2022},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.203121Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:e49ed8803d261ae1c6bea6c46a3c24c8708472fdd6093bb74647fc32d709c400","observation_id":"6c7bbefe-75ff-40b8-9e67-8da196b19d24","resolution":{"observed_at":"2026-08-07T12:29:30.969880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:30.799929Z","title":"SAFER- STUDENT for safe deep semi-supervised learning with unseen-class unlabeled data","venue":null,"work_id":"fe48f3aa-47f0-48a7-b593-b0b18012ef61","year":2024},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.251786Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:929f018cb087474aa38868e48f0a7ab0e740e8781db7a54fa40c414b0592b211","observation_id":"0de19b7d-8c52-4577-a64d-f66081b187bb","resolution":{"observed_at":"2026-08-07T12:29:30.843026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:30.603064Z","title":"Using self-supervised learning can improve model robustness and un- certainty","venue":null,"work_id":"e271a08a-698f-4afd-b0ae-2ec91b90183c","year":2019},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.309043Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:7a4fdd8d4d7dcd5e0839b90efdf42d1da8459db4a10b6d75b1eaf6861eefc8c2","observation_id":"c7bfaf24-d990-4aed-8514-491a045c5681","resolution":{"observed_at":"2026-08-07T12:29:30.736049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:30.283002Z","title":"Trash to treasure: Harvesting OOD data with cross-modal matching for open-set semi-supervised learning","venue":null,"work_id":"444c883b-d94d-4233-bdc9-380580678c9e","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.362618Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:bef50eecd1e7c7deb9a7baeea8d63c12bbcb9257380ed10ab347173cdb98d0e0","observation_id":"626f1021-5291-4fbd-92b3-615b8eb3fa53","resolution":{"observed_at":"2026-08-07T12:29:30.440224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:30.075745Z","title":"They are not completely use- less: Towards recycling transferable unlabeled data for class-mismatched semi-supervised learning","venue":null,"work_id":"5a3eef03-9fb5-4615-b301-9ce9b31918cb","year":2022},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.447107Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:701ee6690294d7a14c04d8f0b1523493c655dd406c860688c7c2cc7974c31d4e","observation_id":"7a5a3c3c-abdb-4fe1-a60b-53e6c20bff8e","resolution":{"observed_at":"2026-08-07T12:29:30.171358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:29.874694Z","title":"Label propagation for deep semi-supervised learning","venue":null,"work_id":"a78f2c58-494d-49ed-90f2-ce38cd477f59","year":2019},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.473298Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:b8a47eca7d352b083a41eb907cbbed36c6a112e6b6f32275b6ff45b25b80931f","observation_id":"beca0523-1d40-474e-bb29-58380b634678","resolution":{"observed_at":"2026-08-07T12:29:29.980220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:29.544370Z","title":"Unknown-aware graph regularization for robust semi-supervised learn- ing from uncurated data","venue":null,"work_id":"2ee5ea50-80fc-4ca9-8665-d00a6935ecbf","year":2024},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.565270Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:86270d8aa90955592153f46ab4320012c8a9d0aeaece60ca7f2158b5e257726e","observation_id":"26dcde5d-8efc-4b5c-9a2d-93085458242c","resolution":{"observed_at":"2026-08-07T12:29:29.731881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:29.337685Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"70623c98-3af0-4ae9-a112-a282141e7a92","year":2009},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.629274Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:807829b843661bb3cff172c48e7111c2956a098ce40eb8b8254f1f6bdae64e4c","observation_id":"9f485a46-2b2a-4f97-8dbf-b15306ff7d2f","resolution":{"observed_at":"2026-08-07T12:29:29.398443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:29.110417Z","title":null,"venue":null,"work_id":"90685b15-9c10-4dee-b440-5a0e1e0f1963","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.705529Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:5f5c91ed524a9caf8c4b84f50abf4761ed5b0ebe3516472f2799ec18453ff4d5","observation_id":"e31f4c61-0900-455f-80cb-2e6f0b7cf619","resolution":{"observed_at":"2026-08-07T12:29:29.239297Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.886998Z","title":"Temporal ensembling for semi-supervised learning","venue":null,"work_id":"0df536ec-8200-4b59-a647-25b619446f8d","year":2017},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.784957Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:c5f410b168125c01c7231df50237d2e6b71688b44583ef402c5e975a7869152a","observation_id":"38eb2a9d-f090-4e78-970d-a37f0526819b","resolution":{"observed_at":"2026-08-07T12:29:28.973030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.713969Z","title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","venue":null,"work_id":"cfaaaefe-0590-42e4-a197-6254e8539309","year":2013},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.856170Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:2c570ad28f3e6607a6b6aa1e1db6535859e24c052f94dff16e100272b27029ba","observation_id":"ad154a59-adb8-4ca5-980c-d72bf991da43","resolution":{"observed_at":"2026-08-07T12:29:28.788044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.568206Z","title":"Diversify and disam- biguate: Learning from underspecified data","venue":null,"work_id":"57b3c7aa-408d-4383-ab2d-14e9b145e8f7","year":2022},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:19.959616Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:12031e1fb8696534d9804b399d363444d9147a74b2f1a1f356fd4cdff257f0e0","observation_id":"128d00d1-4c0f-4853-96c4-16596ee9cef3","resolution":{"observed_at":"2026-08-07T12:29:28.650031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.377851Z","title":"Diversify and dis- ambiguate: Out-of-distribution robustness via disagreement","venue":null,"work_id":"b846e507-b74d-4f8b-8d8f-5de9f57d04f4","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.034269Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:c57a674e10813cd8a0adb29606d489d5e0f5c1f1e30ff105020b9b5855083d0b","observation_id":"c5f9b04e-83de-4048-b5ae-3e21904652d6","resolution":{"observed_at":"2026-08-07T12:29:28.439966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.259808Z","title":null,"venue":null,"work_id":"84637ed9-e014-4b35-abc5-0d7da7cde273","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.164750Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:deb6cc252836173d17c44f172af19e826955a2391816126466af2fc65ffc5233","observation_id":"4f9bd6dc-76b7-4d02-b3c4-4a9362157232","resolution":{"observed_at":"2026-08-07T12:29:28.317426Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:28.115813Z","title":"Iomatch: Simpli- fying open-set semi-supervised learning with joint inliers and outliers utilization","venue":null,"work_id":"9082c449-5b6b-4886-9927-0bc0c74ea00d","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.225061Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:bdf6abdba972b4f1b6ed4ce4f2f82425cace733d13426a28243e499c1bb826f8","observation_id":"30765845-4cbe-40b6-ba6d-04a11aaf8355","resolution":{"observed_at":"2026-08-07T12:29:28.178041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:27.904747Z","title":"Rethinking safe semi-supervised learning: Transferring the open- set problem to a close-set one","venue":null,"work_id":"9d0b1d78-fc09-47bf-b0e4-ab55ee952748","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.324817Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:f6411ff0ff79e934b93c2340ab3b9ed6af2cc18ea5a9a7eb1f1163bf34cb2f3c","observation_id":"c62be277-00ef-475a-8182-18ffd49e87a8","resolution":{"observed_at":"2026-08-07T12:29:28.009046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-07T12:29:20.534753Z","title":"Umap: Uniform manifold approximation and projection for dimension reduction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.534753Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:fc44eb7c3bf0342552f8b7f20836d191bff2844a1306430eb6114455042b6837","observation_id":"27037311-4bf6-48c1-b5d1-2c0d146c241c","resolution":{"observed_at":"2026-08-07T12:29:20.534753Z","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":"2026-08-07T12:29:27.590978Z","title":"Goodfellow","venue":null,"work_id":"c46e5421-df1b-461f-aa04-c3ae6ca052ec","year":2018},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.724736Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:dfbc9384deb8068baffa761f53b13dee0567ee02fdd00b88a90c755770b0d398","observation_id":"11e64503-8979-42dd-823b-5a583d61830c","resolution":{"observed_at":"2026-08-07T12:29:27.745181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:27.409554Z","title":"A threshold selection method from gray-level his- tograms","venue":null,"work_id":"ecf358fc-49c9-42d0-919f-5f693ddf77a0","year":1979},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.834739Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:548ded1a7a2260d0c0ed06f0e51ddbbbac1798ee113deb7e1c0a8d9e26702085","observation_id":"6af75e5b-3377-4486-9dc6-ff92ccfc4fb8","resolution":{"observed_at":"2026-08-07T12:29:27.496104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:27.239204Z","title":"Openmatch: Open-set consistency regularization for semi-supervised learning with outliers","venue":null,"work_id":"91d802b1-9f92-45b9-8650-0a41d54c0d6e","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:20.924736Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:0b74e73333824fb5d52864ec53fb8901b11b82858e08311a9d17dd6167612035","observation_id":"18d91cef-b07f-4c55-befb-1a167f088b92","resolution":{"observed_at":"2026-08-07T12:29:27.344816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:27.079975Z","title":"Regulariza- tion with stochastic transformations and perturbations for deep semi- supervised learning","venue":null,"work_id":"320bcd69-4680-462d-ac64-733aa2adc0cc","year":2016},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.030504Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:d78d7fe3b13a6e7d8b9ebbadd3b28aa8f0b580250362c80a9c10d1e059ce78fc","observation_id":"f21b6d00-40b7-4609-b16a-a005f2f39488","resolution":{"observed_at":"2026-08-07T12:29:27.148077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:26.836342Z","title":"Fixmatch: Simplifying semi-supervised learning with consis- tency and confidence","venue":null,"work_id":"e5af877e-f4ca-4f1c-bc4c-b1b3a0dc3280","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.131096Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:8dc5c52cdc92f2a95830f3ad8ca4c501554e3a51f90271e4c8a236a066bac635","observation_id":"acabfbd4-c711-43b2-aa0a-ef6096621506","resolution":{"observed_at":"2026-08-07T12:29:26.969813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:26.499126Z","title":"Graph-based semi-supervised learning: A comprehensive review","venue":null,"work_id":"39c5f358-2bd6-4af3-a23c-ad691df4adb2","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.295855Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:f0a0a93ffb6fdd56399e286cb6017ce3621067d1b5c6f9368220265cae59285a","observation_id":"1976e2e0-d1cf-43db-93d5-86069a31e3ef","resolution":{"observed_at":"2026-08-07T12:29:26.685428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01364","last_updated":"2021-06-02T17:59:41Z","snapshot_observed_at":"2026-07-06T11:15:20.397336Z","submitted_at":"2021-06-02T17:59:41Z","title":"The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop","version":1},"cited_work":{"arxiv_id":"2106.01364","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.01364","snapshot_observed_at":"2026-08-07T12:29:23.394749Z","title":"The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop","venue":"cs.CV","work_id":"ee7336c7-74c9-4099-be28-2e75da07dc2e","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.534753Z"},"links":{"cited_paper":"/paper/2106.01364","citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:c6eb32c23e57f84f5d6f6d1bd26fdb819425bae1f08b31aade44340ddb49d36c","observation_id":"04bf9a01-3680-48b5-986a-d4777138b66f","resolution":{"observed_at":"2026-08-07T12:29:23.571341Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:21.746232Z","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.746232Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:59dee30ad390ab51f903e5fbfff317810e344fb4beb4af952d00fd07079fe282","observation_id":"6e32e738-b673-4383-aea7-0c9cc59aecdc","resolution":{"observed_at":"2026-08-07T12:29:21.746232Z","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":"2026-08-07T12:29:26.257642Z","title":"USB: A unified semi-supervised learning benchmark for classification","venue":null,"work_id":"47256bb9-cc59-45c5-a41a-6ecfed4233e4","year":2022},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.814741Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:996584b5b0dcaab7e16c9ea4412cee72a6e39282e0145d7af6ccc640051bbd10","observation_id":"574623ad-19f7-43e7-a96a-9b15767b4296","resolution":{"observed_at":"2026-08-07T12:29:26.389864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:26.001132Z","title":"Freematch: Self-adaptive thresholding for semi-supervised learning","venue":null,"work_id":"a70983c9-488b-45b8-884f-fee8dcbe4b9b","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:21.990539Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:cca8e0920cfe5a4362c861ae9703b2b0afe133cf9e752d18503220772544bc4b","observation_id":"236c7eb8-bbd4-4740-90f3-819aa75af0b6","resolution":{"observed_at":"2026-08-07T12:29:26.119776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:25.775146Z","title":"Out-of-distributed semantic pruning for robust semi-supervised learning","venue":null,"work_id":"b066e1a2-3271-416b-bf5e-cc8dcb97543b","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.088760Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:c87da51671c99a18f9cec35bf0bcc9cd11286f3e79282b7a5e9851e2f20eb01f","observation_id":"015c71f0-8189-41bc-a459-c00ad0dda360","resolution":{"observed_at":"2026-08-07T12:29:25.885353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:25.404818Z","title":"Scomatch: Alleviating overtrusting in open-set semi-supervised learning","venue":null,"work_id":"4f7ca6b8-dde9-4f35-9512-41a04f9910f4","year":2025},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.215626Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:b1359c4a928cabde6b9d542be953001ab65f4ca84645e193c7bd7a099b01306b","observation_id":"1d4ca92f-a05a-43a7-baa9-33b71885050d","resolution":{"observed_at":"2026-08-07T12:29:25.554751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:25.161927Z","title":"Hovy, Thang Luong, and Quoc Le","venue":null,"work_id":"d36406de-bd1e-437b-aced-b2c2f4c07dad","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.344348Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:9cd71d77254ad7f975b169b0166d063b9b9b0626d6e97e02955cd60bf6a22e28","observation_id":"15394abd-0254-4fcf-b90b-74110ff5efb3","resolution":{"observed_at":"2026-08-07T12:29:25.240731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:24.904746Z","title":"Self-training for class- incremental semantic segmentation","venue":null,"work_id":"52a7512f-1b0e-43b5-8d0e-a464b4d0476a","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.425310Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:938262fe7c5d82debe827d4577ba83cd24c35564e1ef10e3dbfff4a743c6d355","observation_id":"9ea48db4-7096-4e56-a61d-ed5c04969bd6","resolution":{"observed_at":"2026-08-07T12:29:24.996433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:24.626066Z","title":"Multi-task curriculum framework for open-set semi-supervised learning","venue":null,"work_id":"e5103484-21e4-4434-9ff2-81bd741216d9","year":2020},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.594750Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:9843188682d8bcf3122732f661555611b4ee1759c67150a6a82e3313d804ebbf","observation_id":"6ab41d61-2854-4a62-9db5-0f7324a6fd55","resolution":{"observed_at":"2026-08-07T12:29:24.754748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:24.394508Z","title":"Wide residual networks","venue":null,"work_id":"ffcc0fd5-4966-40b6-9ca6-73fff96a5d9f","year":2016},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.755209Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:e659bc355a179c2c59fa9d55b24eb3093d1aa9f4e42daad01e1b0cb64a583763","observation_id":"66d634d1-229e-4cbb-976b-9690f09518ba","resolution":{"observed_at":"2026-08-07T12:29:24.524741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:24.134249Z","title":"Flexmatch: Boosting semi- supervised learning with curriculum pseudo labeling","venue":null,"work_id":"709898b1-c335-4dfb-b8c5-9be8a22d07cd","year":2021},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.854850Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:d32a913632c631450fc490a07f10d16a0fceffa431b4f3dbc5c2bf82912b7bd5","observation_id":"14ca24c5-48fd-4261-9bfc-308e7f1779d6","resolution":{"observed_at":"2026-08-07T12:29:24.231593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:23.877895Z","title":"Simmatchv2: Semi-supervised learning with graph consistency","venue":null,"work_id":"f589f845-b599-4341-a036-5414332c496d","year":2023},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:22.975006Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:e54c22737b2f13d8c2d9682f084305e5bb49b8bdc1cb57288e346cff1f1a47c7","observation_id":"da586b3a-57f6-4297-a3d0-8ae834f5ad56","resolution":{"observed_at":"2026-08-07T12:29:24.005146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:29:23.704479Z","title":"Simmatch: Semi-supervised learning with similarity match- ing","venue":null,"work_id":"2fc824d5-30c2-4ece-b72e-dd21ec1eda0c","year":2022},"citing_paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:23.083180Z"},"links":{"citing_paper":"/paper/2505.24443"},"observation_digest":"sha256:5bc4319bf8107272d8a2be2d11d8d5fa11dbfef2a19c7c771865fcac153ee44b","observation_id":"917714fd-aafd-4e1a-ace4-08bbe9da3ad0","resolution":{"observed_at":"2026-08-07T12:29:23.771232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24443","last_updated":"2025-05-30T10:24:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T22:58:00.322848Z","submitted_at":"2025-05-30T10:24:30Z","title":"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":48},"total_outbound_references":56},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2505.24443."}