{"as_of":"2026-08-21T18:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b440cec7da3f4dd7f106f0730c0870713bd82445915fee59774f5eeaabd7aa12","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T23:35:45.607156Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"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/1907.04572/citation-record","integrity":"/paper/1907.04572/integrity","json":"/paper/1907.04572/citation-record.json","paper":"/paper/1907.04572"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Cho, S","venue":null,"work_id":"19d8a118-70a4-464d-a2ae-c925c8e94b8c","year":2015},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:0d6df9af624a6999fab19f063192edf6b4d8a3c7bd8ea9715c2833beffdcdaa4","observation_id":"bfa547a1-0c24-4ed3-88d4-30d78ce2db3b","resolution":{"observed_at":"2026-05-24T23:36:27.835365Z","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":"1810.01392","last_updated":"2019-05-23T23:48:06Z","snapshot_observed_at":"2026-08-16T01:12:24.218734Z","submitted_at":"2018-10-02T17:32:07Z","title":"WAIC, but Why? Generative Ensembles for Robust Anomaly Detection","version":4},"cited_work":{"arxiv_id":"1810.01392","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.01392","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"WAIC, but Why? Generative Ensembles for Robust Anomaly Detection","venue":"stat.ML","work_id":"0349f1fa-163b-45aa-bbcc-eca364c62b83","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1810.01392","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:c10c46c1363389c86df3d5b779f2b15d66133bad77d2eac2b61d71094f7b2656","observation_id":"254cab17-528b-4cef-b3c2-404990d3c119","resolution":{"observed_at":"2026-05-24T23:36:27.602197Z","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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4dfab0be-2a81-41e2-a6b1-62cb69f3e0e9","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:ffa2c9f85e6969603a1a270396b7e0d376813cb5cf21063977f61f8933d0a5ad","observation_id":"bfa947e5-5ebd-4661-8d62-9ddb4e0772b4","resolution":{"observed_at":"2026-05-24T23:36:27.842418Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f45ebb45-4c2c-4e6b-97b6-1b16ca6d91c4","year":2017},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:8b583d420423a069cf154c1e46a4857629d60dc6e1977b844e82ee756884f8a3","observation_id":"f53720b9-e248-43e9-815e-68027c7f37c0","resolution":{"observed_at":"2026-05-24T23:36:27.826108Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1812.04606","last_updated":"2019-01-28T20:34:44Z","snapshot_observed_at":"2026-08-14T17:45:07.132382Z","submitted_at":"2018-12-11T18:49:50Z","title":"Deep Anomaly Detection with Outlier Exposure","version":3},"cited_work":{"arxiv_id":"1812.04606","doi":"10.48550/arxiv.1812.04606","metadata_source":"pith","pith_arxiv_id":"1812.04606","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep Anomaly Detection with Outlier Exposure","venue":"cs.LG","work_id":"c69a7f26-b535-4ee9-923f-d7e8ccfb1123","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1812.04606","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:aea1d3c241646eca85e7dd9c4ebc7c5ddd865a48f236f37b36b7d51c5d62ce66","observation_id":"13b0bc9e-7920-41b0-b3b0-354f622c1b12","resolution":{"observed_at":"2026-05-24T23:36:27.612213Z","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":{"arxiv_id":"1811.02657","last_updated":"2019-12-09T10:21:21Z","snapshot_observed_at":"2026-08-21T12:28:02.605379Z","submitted_at":"2018-11-01T01:27:37Z","title":"A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model","version":2},"cited_work":{"arxiv_id":"1811.02657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1811.02657","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"B., Anandkumar, A., Jordan, M","venue":null,"work_id":"01b7b975-8ae4-45ab-a116-b69867e42471","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1811.02657","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:a003a3d114ace3403a4380009900bdfee0f772742f166dcaa8de02124382d9e9","observation_id":"42672e72-dbfa-432d-8613-d84a6a15902e","resolution":{"observed_at":"2026-05-24T23:36:27.616850Z","resolver_source":"arxiv_id","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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1cb35eea-becf-45f5-8e72-7c21deab73ae","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:e107225d0b57d7bf95ba3889e7159aa830d75a2598f5c3f5af49a8462639b59f","observation_id":"fbb7fa75-031a-47fe-93c8-7d474e5da962","resolution":{"observed_at":"2026-05-24T23:36:27.822862Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-06-05T21:23:00.469572Z","title":"and Hinton, G","venue":null,"work_id":"5f0c449f-0f3b-42f9-9bde-250d5d11ffc8","year":2009},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:eb39e388c7710945fe4d00d6e312b17ddba8db420755ef44b7665c49fc494006","observation_id":"32fee60f-17a1-45d5-b2f6-cede5d33dec4","resolution":{"observed_at":"2026-05-24T23:36:27.819525Z","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":"1809.04758","last_updated":"2019-01-15T05:13:19Z","snapshot_observed_at":"2026-08-18T22:36:18.968463Z","submitted_at":"2018-09-13T03:54:22Z","title":"Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series","version":3},"cited_work":{"arxiv_id":"1809.04758","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.04758","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series","venue":"cs.LG","work_id":"4795537e-5615-4be3-b46f-3da720bb62e9","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1809.04758","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:9855c11662b8c13aea685763be0dceb4bd827b7e98f5508298bc3b3d21b3e7c7","observation_id":"d93117e9-b73e-4eb4-9365-aadd9496bda1","resolution":{"observed_at":"2026-05-24T23:36:27.597620Z","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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4c88bcdc-9232-49bb-bb38-45156027c93a","year":2015},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:b778225f4566dd39abe8c68750f7ed51db52debe830241522805ff05fb90e7ff","observation_id":"e6294ffd-7f66-45f3-9289-1d9a37559da6","resolution":{"observed_at":"2026-05-24T23:36:27.829122Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1810.09136","last_updated":"2019-02-24T11:57:32Z","snapshot_observed_at":"2026-08-15T00:45:03.614274Z","submitted_at":"2018-10-22T08:32:02Z","title":"Do Deep Generative Models Know What They Don't Know?","version":3},"cited_work":{"arxiv_id":"1810.09136","doi":"10.48550/arxiv.1810.09136","metadata_source":"pith","pith_arxiv_id":"1810.09136","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do Deep Generative Models Know What They Don't Know?","venue":"stat.ML","work_id":"3be6d8c7-0fc4-44b6-b288-39a8f3100ba1","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1810.09136","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:813277a6d88eab0af5ebe312fe39420d080de5d5da8c02320d024ab13039c698","observation_id":"8a07a820-e564-4880-8f5e-e07e30e52246","resolution":{"observed_at":"2026-05-24T23:36:27.592530Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1902.02767","last_updated":"2019-05-29T13:52:04Z","snapshot_observed_at":"2026-08-14T17:19:33.419768Z","submitted_at":"2019-02-07T18:49:47Z","title":"Hybrid Models with Deep and Invertible Features","version":2},"cited_work":{"arxiv_id":"1902.02767","doi":"10.48550/arxiv.1902.02767","metadata_source":"pith","pith_arxiv_id":"1902.02767","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hybrid Models with Deep and Invertible Features","venue":"cs.LG","work_id":"deacab0f-02dc-4bff-a6c8-0b4d527a3fb5","year":2019},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1902.02767","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:89b80cee2bbac7e9f4f87c0865d982cebdcf1e3b49833493f6ab82421058701a","observation_id":"f57f6b68-5d60-44a3-a8d4-6727c7674b42","resolution":{"observed_at":"2026-05-24T23:36:27.603430Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7764fedc-f03e-4771-9dc8-aa4c100d4046","year":2011},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:e541f54704be32ab7f98eb99c12c078ceebb32222645d03b3e1da979379dd8c4","observation_id":"a5193bcb-3803-4f11-9f44-46a49979f07b","resolution":{"observed_at":"2026-05-24T23:36:27.832224Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1412.6806","last_updated":"2015-04-13T07:58:17Z","snapshot_observed_at":"2026-08-18T17:02:01.677257Z","submitted_at":"2014-12-21T16:16:37Z","title":"Striving for Simplicity: The All Convolutional Net","version":3},"cited_work":{"arxiv_id":"1412.6806","doi":null,"metadata_source":"pith","pith_arxiv_id":"1412.6806","snapshot_observed_at":"2026-07-04T05:59:36.928402Z","title":"Striving for Simplicity: The All Convolutional Net","venue":"cs.LG","work_id":"31127b64-cba9-4eeb-8256-888751a69827","year":2014},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1412.6806","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:5174db0ecec820d470ddbd616924fb1a2754468c3e5f336ace3f62d5e4d70cf8","observation_id":"f723d2fc-5a08-4590-89f7-fc5e4e38b16a","resolution":{"observed_at":"2026-05-24T23:36:27.576473Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1311.2901","last_updated":"2013-11-28T23:04:01Z","snapshot_observed_at":"2026-08-14T23:55:35.657994Z","submitted_at":"2013-11-12T20:02:22Z","title":"Visualizing and Understanding Convolutional Networks","version":3},"cited_work":{"arxiv_id":"1311.2901","doi":"10.48550/arxiv.1311.2901","metadata_source":"pith","pith_arxiv_id":"1311.2901","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Visualizing and Understanding Convolutional Networks","venue":"cs.CV","work_id":"2af3d77f-49d1-4bf9-ac8b-c9e9762b4b26","year":2013},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1311.2901","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:9a521db1deb3558f1996283d57e8853c51d96f85832471919bf3cff7883101cb","observation_id":"77d0c706-b913-4efe-a41a-d1bfcd9fa06c","resolution":{"observed_at":"2026-05-24T23:36:27.622942Z","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":{"arxiv_id":"1802.06222","last_updated":"2019-05-01T21:54:16Z","snapshot_observed_at":"2026-08-19T21:21:23.269481Z","submitted_at":"2018-02-17T11:26:53Z","title":"Efficient GAN-Based Anomaly Detection","version":2},"cited_work":{"arxiv_id":"1802.06222","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.06222","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient GAN-Based Anomaly Detection","venue":"cs.LG","work_id":"59bf6a1a-35db-4af7-8bd8-efd9b1a94a4b","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1802.06222","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:5671db0965c40277d0683535a0b4b3b267fe5c085ff4d56cb68a3765bcdca082","observation_id":"e875e52e-c21d-40f9-bf3f-264b3b94e6b0","resolution":{"observed_at":"2026-05-24T23:36:27.611454Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-06-05T21:23:00.469572Z","title":"cat\" and reconstruction of cat from false label","venue":null,"work_id":"5b66c779-4a59-4612-b9b8-5dbfef47501a","year":2017},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:59cd26384584b92b0323bcfae5f3ddea36e92f0d7586c0f15e8bcc3ded0c2229","observation_id":"0f2abc27-181f-4ca9-8afc-fb413b7f464d","resolution":{"observed_at":"2026-05-24T23:36:27.839110Z","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":"1907.04572","last_updated":"2019-07-10T08:32:53Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":5,"verified_exact":5,"verified_fuzzy":3},"total_outbound_references":17},"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 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:1907.04572."}