{"as_of":"2026-08-21T14:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f60281b94b54b9176d63460eee9157f1891680759a8f6915209f26e86acfd6d","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:27:00.321986Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.01640/citation-record","integrity":"/paper/2501.01640/integrity","json":"/paper/2501.01640/citation-record.json","paper":"/paper/2501.01640"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:27:01.655879Z","title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","venue":null,"work_id":"ab4c373c-b554-46af-9464-126218d7ea90","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.793768Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:8fbfbd58de1c4f969dcc5abfcc58de295aaede35d766b4211a582a124290eac5","observation_id":"021c295e-53a2-4499-ac50-33bc08d43da6","resolution":{"observed_at":"2026-08-10T22:27:01.662774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.630780Z","title":"Uncertainty esti- mation based adversarial attack in multi-class classification","venue":null,"work_id":"865be21e-e2e0-4337-be5f-172f888a4320","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.802387Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:b270131552ab34f8c7815a104ce42b4b03200ae4c575514bdc97b0c14d33f13d","observation_id":"f4b1f8a8-c271-49a7-908f-5320780ea45e","resolution":{"observed_at":"2026-08-10T22:27:01.638706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.610866Z","title":"Source-free domain adaptive fundus image seg- mentation with denoised pseudo-labeling","venue":null,"work_id":"9acd203a-a5f1-4c61-aac3-c185342368a0","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.810182Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:6db7f9fa053a2240bbdd8ddb5e80121fca400c9c70c48e40d5e3e7a1ce6b84af","observation_id":"73f4c0be-37d1-482f-ba71-9770827ca8a2","resolution":{"observed_at":"2026-08-10T22:27:01.616816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.590075Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","venue":null,"work_id":"c0e4368c-f963-43aa-9f66-d134300f2e33","year":2018},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.828978Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:6927f275c0945f46a7a2df5f941a07233d2322ff8abd80664765441df68bdfa8","observation_id":"e46c5627-1020-4007-9870-f66be499fb75","resolution":{"observed_at":"2026-08-10T22:27:01.597363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.570281Z","title":"Semi-supervised semantic segmentation with cross pseudo supervision","venue":null,"work_id":"116a8421-025a-4ce1-a249-ed837da9f829","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.844736Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:32828d84708f030752ac4e8685cc7c8b47b24d24db5c2b65e621e29a8ccfe4d5","observation_id":"89237d7c-c536-41da-ac5c-b8d979b5ced7","resolution":{"observed_at":"2026-08-10T22:27:01.576277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.550477Z","title":"Uncertainty estimation in deep neu- ral networks for dermoscopic image classification","venue":null,"work_id":"b239fcc7-36e9-4279-8adc-05e62871a35d","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.858631Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:ebb4b77df0a5345320984dc028fd959d07c0f03cee8aeae0a43fcbadc64df813","observation_id":"d3f147b3-9ed9-49c7-aa88-a46fad5d6633","resolution":{"observed_at":"2026-08-10T22:27:01.556114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.527693Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":"f3234bd6-c475-4a71-a382-33b9946c519f","year":2016},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.871516Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:25e082b682b4fc3f4850147493b7efc0a07f2c8d80f29671ade8b3c21a4b87d1","observation_id":"2085ba4a-8666-4ae6-becc-dd0eb0cf3f31","resolution":{"observed_at":"2026-08-10T22:27:01.535264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:26:59.882693Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.882693Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:62d3a0e5590d88e7197ecdee6ae0734620f7f39adb98e1ef831df2465c433b52","observation_id":"7590eae8-f669-446a-907a-111f1674ccfc","resolution":{"observed_at":"2026-08-10T22:26:59.882693Z","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":"2026-08-10T22:27:01.485460Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"023aa78f-bd9e-4e76-89a5-c72c2e2cf096","year":2015},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.894884Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:50f9f877a197d93f3758b2e42d6524632c8bb60cf130eb6541904ac2330762a4","observation_id":"57931bce-9cbc-4caa-b9f8-9998a00d192c","resolution":{"observed_at":"2026-08-10T22:27:01.492177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.456557Z","title":"Camouflaged object detec- tion","venue":null,"work_id":"f2a84bea-b732-4220-93f0-bf3f2734debc","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.905254Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:88f139a91eddd0d13e36711e0fc88517bc557d5bc92a91de1f5a09354e41a63c","observation_id":"a60fd39e-5ed7-4fd4-80d3-9853ec4a0296","resolution":{"observed_at":"2026-08-10T22:27:01.464794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.435561Z","title":"Conservative-progressive collab- orative learning for semi-supervised semantic segmentation","venue":null,"work_id":"0c3091aa-a85f-444a-ba15-7bade093eff2","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.914695Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:3fb17c9f46b2de1a8878cc15ffdf9e9f94dd23b0f97bb5c8f5694b39182ca027","observation_id":"fc536344-dcf0-478b-ad33-2e7cce5bff8c","resolution":{"observed_at":"2026-08-10T22:27:01.442687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.419392Z","title":"Semi-supervised semantic segmen- tation needs strong, varied perturbations","venue":null,"work_id":"a721b01b-fd28-4271-a780-5381a233ae9e","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.923365Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:cfab096462959a803e20590eed2f3111dafdb75f5b52258b37a97eeafe06f443","observation_id":"e23e7614-27d5-4f36-8c1a-071e805bf23f","resolution":{"observed_at":"2026-08-10T22:27:01.424248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.401853Z","title":"Your classifier is secretly an energy based model and you should treat it like one","venue":null,"work_id":"908d691a-b10f-4176-8e4c-abff183b8ddf","year":2019},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.934286Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:42fd7b97d830cbeec6b89bf1fb1787844ddd0208849649fdee2f6cbc8d2c1ffb","observation_id":"8c24716f-0c51-44ca-8d66-a7728ae0ba04","resolution":{"observed_at":"2026-08-10T22:27:01.407369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:26:59.941942Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.941942Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:4792f6c996e34759e3dc8b52a380209ebe4b1a9cd27140c7990e45562a27ede9","observation_id":"bdc58330-88ad-4661-973a-0975a7f3dd0d","resolution":{"observed_at":"2026-08-10T22:26:59.941942Z","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":"2026-08-10T22:27:01.372438Z","title":"Semicvt: Semi-supervised convolutional vi- sion transformer for semantic segmentation","venue":null,"work_id":"2dab1373-7d09-4203-8092-b7d319e91ead","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.954568Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:b7a8c7956ec1612383fd3cbfc71c770b885263f25d76825505bcbfb23f20d161","observation_id":"0159065b-c0f7-406b-a686-86085d7857c8","resolution":{"observed_at":"2026-08-10T22:27:01.378125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07934","last_updated":"2018-07-24T22:56:44Z","snapshot_observed_at":"2026-08-14T19:43:26.498400Z","submitted_at":"2018-02-22T08:13:20Z","title":"Adversarial Learning for Semi-Supervised Semantic Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07934","snapshot_observed_at":"2026-08-10T22:26:59.964333Z","title":"Adversarial learning for semi-supervised semantic segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.964333Z"},"links":{"cited_paper":"/paper/1802.07934","citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:629063d10596d2d665b02dbb2022466ab7f4ed2982ef7baf1d135211edeb8d21","observation_id":"36b63382-c4a3-4bfa-a483-4ac36e05995b","resolution":{"observed_at":"2026-08-10T22:26:59.964333Z","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":"2026-08-10T22:27:01.343394Z","title":"Data uncertainty guided noise-aware preprocessing of fingerprints","venue":null,"work_id":"5ae484e0-938a-4fc2-9325-a341c40a90ad","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.973026Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:9db2e646136e016980772d2a96384c3c2ff2564916ff494af8b370852f3615e2","observation_id":"1d2e6956-edca-4dc5-8749-fb666413d0b5","resolution":{"observed_at":"2026-08-10T22:27:01.355955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.312003Z","title":"Guided collaborative training for pixel-wise semi-supervised learning","venue":null,"work_id":"30dda798-5575-4081-986b-b211c34703f0","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.990068Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:d91ff644e05b5075ee29293806305ca4e98b41f6f5c6aefc93737f4159a21626","observation_id":"c3f59233-7232-4b84-aac0-ea7d60f484d1","resolution":{"observed_at":"2026-08-10T22:27:01.323158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.286976Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017","venue":null,"work_id":"767ac6e0-5873-4199-90d9-192f0e44db1d","year":2017},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:26:59.999399Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:0ba73a9aaac1b8abe47d30fa4e9691ca1c91d9ecdaf112f32fc875e99cc4de51","observation_id":"49d106ef-c2f6-4596-828a-b5bf7067dd54","resolution":{"observed_at":"2026-08-10T22:27:01.294120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.261852Z","title":"Pruning-guided curriculum learning for semi- supervised semantic segmentation","venue":null,"work_id":"06104379-64b3-4369-806b-6cdc37519573","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.009237Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:54464b712cb6ca7f7a7523bd44fa713f63fc573aafab80c3a8cc584464e9c198","observation_id":"2a5d6ee2-e4b1-4f6e-9e04-9c28fea9be00","resolution":{"observed_at":"2026-08-10T22:27:01.271082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00727","last_updated":"2021-07-01T20:08:15Z","snapshot_observed_at":"2026-08-20T01:44:49.248228Z","submitted_at":"2021-07-01T20:08:15Z","title":"Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation","version":1},"cited_work":{"arxiv_id":"2107.00727","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.00727","snapshot_observed_at":"2026-08-10T22:27:00.467928Z","title":"Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation","venue":"cs.LG","work_id":"4f8ae6ce-bf75-4a6b-8728-6ddc78ae2268","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.016725Z"},"links":{"cited_paper":"/paper/2107.00727","citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:5feb041d66fbda28110464b5162f6d434484132298b0e92c695dd538248a594f","observation_id":"45408447-4e74-46df-a852-1cdd4b368651","resolution":{"observed_at":"2026-08-10T22:27:00.480256Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.233987Z","title":"Kurmi, Venkatesh K","venue":null,"work_id":"5174958d-1ff3-486b-a220-f73b16e3ce27","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.055398Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:c40c5bc7d184961ff274914cd5a84eda5a2dc37f7176c1716a862ed9a0bcfadf","observation_id":"77dc1551-958a-4f9f-b1a0-a729c1874099","resolution":{"observed_at":"2026-08-10T22:27:01.242429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.215379Z","title":"An overview of mixing augmentation methods and augmentation strategies","venue":null,"work_id":"97845090-8fdf-4100-8761-847393c904d0","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.068618Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:0309bfec03521c5f593b2ef3b6d62526be515df71e7ab00a456317a51094cd8b","observation_id":"cd5723a0-ea8f-4078-9084-8cc3e56068ed","resolution":{"observed_at":"2026-08-10T22:27:01.222269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.189700Z","title":"Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization","venue":null,"work_id":"984b4074-c6bc-457f-a39c-3bff1aa894e4","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.082970Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:9706f768ff5ae224aeff24d1c864c9cee2cd7cdc2f9e965bd6899a2c96fb75e0","observation_id":"56101541-edfb-468c-93cf-e34d2be68a64","resolution":{"observed_at":"2026-08-10T22:27:01.195634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.169192Z","title":"Semi-supervised semantic segmentation under label noise via diverse learning groups","venue":null,"work_id":"5df28acb-0f2d-4dc9-9905-545b3ffdb647","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.094784Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:567d2c97c52388a07abf001ae390cfe926912e7938abdd8acd21a8607bc646c0","observation_id":"7771809e-81c3-4296-8d0e-7c805e0c01d6","resolution":{"observed_at":"2026-08-10T22:27:01.175522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09281","last_updated":"2023-08-18T03:46:29Z","snapshot_observed_at":"2026-08-20T01:44:54.444988Z","submitted_at":"2023-08-18T03:46:29Z","title":"Diverse Cotraining Makes Strong Semi-Supervised Segmentor","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09281","snapshot_observed_at":"2026-08-10T22:27:00.107807Z","title":"Diverse cotraining makes strong semi- supervised segmentor","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.107807Z"},"links":{"cited_paper":"/paper/2308.09281","citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:78056db0580468fce8aaf0b08053fa93e87f9e6390a9ca55f013b886eddafc9d","observation_id":"fd4f58ef-f994-45d7-aa77-0083cfd0abb7","resolution":{"observed_at":"2026-08-10T22:27:00.107807Z","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":"2026-08-10T22:27:01.144477Z","title":"A general framework for uncertainty estimation in deep learn- ing","venue":null,"work_id":"cdc0aa20-38bd-46b6-893c-921ad8e2c13a","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.117805Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:22fbb942fc17d09306a48caa61b6a6cdf5f576abb5e93c8d91f9fc9a2d58d352","observation_id":"cf242cad-1b2e-4412-a807-0bc23f00851a","resolution":{"observed_at":"2026-08-10T22:27:01.150925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.111553Z","title":"Uncertainty-aware pseudo-label and consistency for semi- supervised medical image segmentation","venue":null,"work_id":"986b445d-1aea-481b-bdc1-8f84644c70e1","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.145156Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:eca21a4df3bae890d209169b26223c12ca1e4ac5e574cf1290867bc00abb1624","observation_id":"ed8681eb-31e5-4285-babe-7297a96c13cc","resolution":{"observed_at":"2026-08-10T22:27:01.128184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.081252Z","title":"A survey on uncer- tainty estimation in deep learning classification systems from a bayesian perspective","venue":null,"work_id":"7050e28e-d9f1-4ff7-aeef-f60052ec4c23","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.155084Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:a9fcc90ae36ed9c10b2f477e67791b1467b98f249e470b1fb5ba446b606da8ec","observation_id":"9f5cbd07-079f-4174-893f-3dc805e57602","resolution":{"observed_at":"2026-08-10T22:27:01.088149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:01.045525Z","title":"Image seg- mentation using deep learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(7):3523– 3542, 2022","venue":null,"work_id":"0c86fc16-20a0-4774-8049-2a217c41a51a","year":2022},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.164648Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:cb07bd7f92c9c3cb78892716e5a7c59e384773622dde6bf7b1397e4caf68937d","observation_id":"c5407e55-0c4c-4943-af12-f1c8c6374de6","resolution":{"observed_at":"2026-08-10T22:27:01.057850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11582","last_updated":"2022-01-28T16:48:08Z","snapshot_observed_at":"2026-08-20T01:41:22.281341Z","submitted_at":"2021-02-23T09:44:09Z","title":"Deep Deterministic Uncertainty: A Simple Baseline","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.11582","snapshot_observed_at":"2026-08-10T22:27:00.173178Z","title":"Deterministic neural net- works with appropriate inductive biases capture epistemic and aleatoric uncertainty","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.173178Z"},"links":{"cited_paper":"/paper/2102.11582","citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:e22820bfd560551b01fd3b16e7cf3d04ea8c0e870c379d348a06c90512493201","observation_id":"a93c8fa1-01ef-41f3-990d-e3cdf3694ae4","resolution":{"observed_at":"2026-08-10T22:27:00.173178Z","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":"2026-08-10T22:27:01.013179Z","title":"Semi- supervised semantic segmentation with cross-consistency training","venue":null,"work_id":"84053e9d-9b17-4e15-857a-51a16414016d","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.183091Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:4d2ad60837d39756cfdeb33e14b29d6bf1647ff7bb03d09af445d7f9a10c227c","observation_id":"84a34987-08d6-4e6b-8155-678983dc1c55","resolution":{"observed_at":"2026-08-10T22:27:01.022241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.982996Z","title":"A survey on semi-supervised semantic segmentation","venue":null,"work_id":"eb06e5f3-1992-41f1-a12b-45ee0921897d","year":null},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.190236Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:f258c637f727473ff7de2aabece852f862d792d8b2c12e796cb66bf1c43b605d","observation_id":"334071dc-70a3-468c-a6a5-49fabcb2ea27","resolution":{"observed_at":"2026-08-10T22:27:00.990428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.919105Z","title":"Inconsistency- aware uncertainty estimation for semi-supervised medical image segmentation","venue":null,"work_id":"53547226-7763-406e-8544-32a89a35b230","year":2022},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.199408Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:b3a9132fd1f61139338098eac08dcb2e8462b3845a5ca4f5d60abce4bd388b1e","observation_id":"8eb0942e-8c56-4e5f-b540-d5117e19f9ed","resolution":{"observed_at":"2026-08-10T22:27:00.926329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.896765Z","title":"Inconsistency- aware uncertainty estimation for semi-supervised medical image segmentation","venue":null,"work_id":"c54bf555-bd85-42b8-922f-534e07d8e7fd","year":2022},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.207548Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:33a9c66b82356a3142f5d93fe21fbbb6acfc6d1dff56a175fec3148871636e9e","observation_id":"c41c012a-b6c4-4756-aaa8-3a83762c4c25","resolution":{"observed_at":"2026-08-10T22:27:00.903511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.215352Z","title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.215352Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:e256b81ebad4ab2e4ff657a8e0c6f1cfbebba14a14ea1512d72bd3f0c6e618a5","observation_id":"c269d332-fac7-4fb5-aeff-f008f933cb0a","resolution":{"observed_at":"2026-08-10T22:27:00.215352Z","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":"2026-08-10T22:27:00.838852Z","title":"Mean teachers are bet- ter role models: Weight-averaged consistency targets im- prove semi-supervised deep learning results","venue":null,"work_id":"e8edddf8-c909-469c-bfe8-b63605a07caf","year":2017},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.223672Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:0ec87bd2561d911fe1f2c5867736b3519fafe9b0c1e8e23d8839b95e42963447","observation_id":"5de1cd5d-6a46-4989-be8f-30e1351fe9f6","resolution":{"observed_at":"2026-08-10T22:27:00.848562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.811242Z","title":"Aleatoric un- certainty estimation with test-time augmentation for medi- cal image segmentation with convolutional neural networks","venue":null,"work_id":"3b37a701-bd51-483e-9d2f-bde054604fcb","year":2019},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.234042Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:4992908d72e6416937c93760427e1c58fab899c1b62fe811c7b8caaa3cb41a34","observation_id":"ee414d32-dc4b-4e5d-82b2-264b2e673e51","resolution":{"observed_at":"2026-08-10T22:27:00.818831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.781696Z","title":"Allspark: Reborn labeled features from unlabeled in trans- former for semi-supervised semantic segmentation","venue":null,"work_id":"8ee56403-59ac-46a7-a5fa-089058ea4355","year":2024},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.244970Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:fc4ad4dc44f89545400abaa50e5358265e258121ab90321625aa7b899e9c16f2","observation_id":"382aeb35-9aa6-4c1e-a412-c00223efdaa1","resolution":{"observed_at":"2026-08-10T22:27:00.787719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.758053Z","title":"Semi-supervised semantic segmentation using unreliable pseudo-labels","venue":null,"work_id":"c4f83236-5785-4d30-8dbe-4363ae1866f3","year":2022},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.254948Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:d6e80d57cf0c67920eb81f7541d124277e69dd9721cf681402f67c360406adc5","observation_id":"aa49b4c5-7aa7-4fc8-96ec-a86cd032f7d0","resolution":{"observed_at":"2026-08-10T22:27:00.767334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.734158Z","title":"Revisiting weak-to-strong consistency in semi-supervised semantic segmentation","venue":null,"work_id":"6429a68e-3444-4c26-ac4b-cff7b46b3985","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.268384Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:6b4593fa1a3781c7f69d80c081600443104d522c371596a7b8669d59f8213cde","observation_id":"07d62ed4-3710-463e-b355-b1fa92853f9a","resolution":{"observed_at":"2026-08-10T22:27:00.741730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.704104Z","title":"Towards bridging the performance gaps of joint energy-based models","venue":null,"work_id":"6603c8e1-9acf-4cab-b413-bf2fb7ba5ac3","year":2023},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.277904Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:182c2023fef063faa8cf84a5da72389ce2e5043be1ce77b8c1c623a4f4fb7317","observation_id":"7e50318e-b632-4c78-abd9-0289f2f9b262","resolution":{"observed_at":"2026-08-10T22:27:00.713904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.669756Z","title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation","venue":null,"work_id":"e59fadd7-6042-49c5-b3e8-1d442c26ee05","year":2019},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.287931Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:6215781d7073fd17b898a667e8d99b2f4d4a694f1f22703d29bc55cee7bd22cf","observation_id":"1558ab8f-d16a-4e5f-87d8-26d0dae2f27f","resolution":{"observed_at":"2026-08-10T22:27:00.679483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.631995Z","title":"A survey of semi-and weakly supervised se- mantic segmentation of images","venue":null,"work_id":"c80c9d60-9efb-47d7-b610-eff21d3f5065","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.297247Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:f34fcafa0704effc3b724069d48b719ac1f95231047b0115483bb892530056da","observation_id":"c0ad703d-2b0f-4977-ba5f-c243cc46cb71","resolution":{"observed_at":"2026-08-10T22:27:00.645654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.606970Z","title":"Joint energy-based models for semi-supervised classifica- tion","venue":null,"work_id":"61e6469f-73ef-491e-86b6-c546bf68071a","year":2020},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.304642Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:c88e30dfd0ad60d3daef920dc189ebecf08d7d968f63798f3fb5d842420c4a3a","observation_id":"b6e0996d-7c69-42c1-9e55-13eefca4022c","resolution":{"observed_at":"2026-08-10T22:27:00.612967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.578622Z","title":"S³mpl:semi-supervised semantic segmentation with mixed pseudo label","venue":null,"work_id":"3316d56b-08b8-49e8-a060-21222043cbec","year":2022},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.312674Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:344d54714583de074fc7d723aa4055d2d483eb101f8bd8881ca6d293303645e1","observation_id":"479f7a11-d6b1-406f-822a-71cc08cc4361","resolution":{"observed_at":"2026-08-10T22:27:00.587508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:27:00.535384Z","title":"Pseudoseg: Designing pseudo labels for semantic segmentation","venue":null,"work_id":"b6efe030-9cbb-4384-9b54-7c3bcf9f9a19","year":2021},"citing_paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T22:27:00.321986Z"},"links":{"citing_paper":"/paper/2501.01640"},"observation_digest":"sha256:1110fa0f5e64b4c0381fd5041da81ce529380575166ffbcecd665c16a760fc2d","observation_id":"1d738b0a-17c7-440c-8534-6a98fc5b9736","resolution":{"observed_at":"2026-08-10T22:27:00.546314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01640","last_updated":"2025-01-03T05:18:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-20T01:41:48.030564Z","submitted_at":"2025-01-03T05:18:38Z","title":"Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":40},"total_outbound_references":47},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.01640."}