{"as_of":"2026-08-17T03:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e1510aa862e0355ef52680df41b5b5f81f73668e65a5f445da60acd9ef814da5","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:44:50.161834Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.02686/citation-record","integrity":"/paper/1908.02686/integrity","json":"/paper/1908.02686/citation-record.json","paper":"/paper/1908.02686"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:44:50.812530Z","title":"Local explanation methods for deep neural networks lack sensitivity to parameter values","venue":null,"work_id":"388d6968-15d6-4cdd-bc25-17bd929532df","year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.955162Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:31250ecea8759f6eb642bfa6116a3ca2807c73ac992908c6a02f6f9bd5fdb63c","observation_id":"b6bf071c-5f84-46d2-ba6b-61ea7d6eea80","resolution":{"observed_at":"2026-08-14T14:44:50.816719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.800981Z","title":"A survey on automated microaneurysm detection in dia- betic retinopathy retinal images","venue":null,"work_id":"6de8f746-d78a-4481-8931-ac52a05f9f61","year":2013},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.959419Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:641a3e6ebec61e5446e090852b39e1443755019846b0431f3ac397bc2351a78f","observation_id":"8cacfd36-1fd9-4f4f-8eb4-19085898f87b","resolution":{"observed_at":"2026-08-14T14:44:50.805057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.790885Z","title":"Arunkumar and P","venue":null,"work_id":"d912ab4e-0b58-4740-8041-c8f35a55d84a","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.963293Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:a6d7e1fd7c235b8319cc3296e685cb9d77a012eb47e20044c89586a4e401b71b","observation_id":"3d86d572-aed9-48ab-a383-079a8ca7d5a0","resolution":{"observed_at":"2026-08-14T14:44:50.794344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.779226Z","title":"On pixel-wise explanations for non-linear classi- ﬁer decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015","venue":null,"work_id":"04943e2a-bbd7-42ac-9e87-05803a13471d","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.966993Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:2691a1f336bf201b018a13878c0757106655ef6b87e6235b8b2153e139645bac","observation_id":"4e3f7abc-ae9b-481c-8d15-a82a0e829cdb","resolution":{"observed_at":"2026-08-14T14:44:50.783068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.767474Z","title":"How to explain individual classiﬁcation decisions","venue":null,"work_id":"5429fb27-28fa-44a2-9469-8ef7e864a978","year":2010},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.971277Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:983d02975142c580492c87c109dca17a04e8abe917fe413729e8da9b2b72edeb","observation_id":"00ca1efb-624e-4b2e-83a7-df19d4907156","resolution":{"observed_at":"2026-08-14T14:44:50.771254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.08024","last_updated":"2019-02-25T16:22:07Z","snapshot_observed_at":"2026-08-16T11:54:37.239860Z","submitted_at":"2018-07-20T20:48:44Z","title":"Explaining Image Classifiers by Counterfactual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.08024","snapshot_observed_at":"2026-08-14T14:44:49.975619Z","title":"Explaining image classiﬁers by counter- factual generation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.975619Z"},"links":{"cited_paper":"/paper/1807.08024","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:29e87bac75067d3ee179b08d685f22a21428978d446b45dbcb6e7c29de6a02ed","observation_id":"512410be-9891-4d7d-8398-113c6353b503","resolution":{"observed_at":"2026-08-14T14:44:49.975619Z","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":"2026-08-14T14:44:50.756849Z","title":"Balasubramanian","venue":null,"work_id":"d193587e-367a-4b95-bcc5-87f7ec029645","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.980344Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:0350ed9a46e6e40f83f4d116798b353b267e218f493f279ec70ca54040aac4ab","observation_id":"0399e714-8fa2-4d3e-a6eb-a851487268d7","resolution":{"observed_at":"2026-08-14T14:44:50.760537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.744024Z","title":"Colas, A","venue":null,"work_id":"4b5723e4-6e86-4437-961f-42e15e959c1f","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.984161Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:eed1ae41e09691bb571b9445ff4ff94d2c13cfda50a78ba4a96c615ddd4ee306","observation_id":"7294c2b3-c7ba-4318-b027-d86c472c82ec","resolution":{"observed_at":"2026-08-14T14:44:50.748791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.732682Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":"542bd244-02f5-4993-8556-829f59bc07ea","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.987669Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:2c5652c6104fa8f7dd35997730347e2e8a2ffbe20c18513ed78246ad85c9125a","observation_id":"ca97e9dd-3b31-499b-b253-d5af8ac70877","resolution":{"observed_at":"2026-08-14T14:44:50.736432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.720591Z","title":"EyePACS: an adapt- able telemedicine system for diabetic retinopathy screening","venue":null,"work_id":"88181142-5ea1-4602-815b-75946648541e","year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.991333Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:8aa98923b3563ea58b00e8ed312764c9c7f138d460044f665aace841d20617be","observation_id":"a0a6acc6-c88a-48dc-ae9c-6a5143087704","resolution":{"observed_at":"2026-08-14T14:44:50.724741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.707613Z","title":"Real time image saliency for black box classiﬁers","venue":null,"work_id":"c88701c1-165c-4f9e-9c48-fcbb0f198a70","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.995581Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:0d9015f3930b8737b8f3b4ca358988c79be85f6d365257efbcf9cb3b4382cb60","observation_id":"40ab4845-8624-43c6-bd79-550cc0009ab1","resolution":{"observed_at":"2026-08-14T14:44:50.712317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.696356Z","title":"Doll ´ar, C","venue":null,"work_id":"6d4ed124-b89b-4075-b8ac-b3f12bbcb8d6","year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:49.999475Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:a8f28f337fa9b2f5f2957023ed0877a6570975299df910d801eb29510109f9f2","observation_id":"ca62b632-ae51-4fa5-b537-3796851a36fb","resolution":{"observed_at":"2026-08-14T14:44:50.700005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.00033","last_updated":"2019-05-19T20:44:37Z","snapshot_observed_at":"2026-08-14T18:45:47.477019Z","submitted_at":"2018-07-31T19:14:39Z","title":"Techniques for Interpretable Machine Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.00033","snapshot_observed_at":"2026-08-14T14:44:50.003790Z","title":"Techniques for interpretable machine learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.003790Z"},"links":{"cited_paper":"/paper/1808.00033","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:dd26096b4efbd5ed9c5bcd11709d2015acb2632e485f690f15797452059f91f7","observation_id":"3221e870-124e-43d0-bd8a-fcbdd65a70e0","resolution":{"observed_at":"2026-08-14T14:44:50.003790Z","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":"2026-08-14T14:44:50.684738Z","title":"To- wards explanation of dnn-based prediction with guided fea- ture inversion","venue":null,"work_id":"51b736a1-d96e-48d1-a987-8bb59b216bd3","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.008466Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:c309650c661f1f4a7f73111f552b83036ec0b337a89c0ad33b9d3489d3227b49","observation_id":"550d0d60-bb08-4bee-8767-d5ef6e3c1f32","resolution":{"observed_at":"2026-08-14T14:44:50.688462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.673759Z","title":"https://www.kaggle.com/c/diabetic-retinopathy- detection","venue":null,"work_id":"2492a726-d66d-4ee5-b9cd-59a2f639bc2d","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.013061Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:8467cf826633e06cabeb7f1577d77194ef2610cb6b7e9188d6c6eea1043de204","observation_id":"f3bdc381-40fb-4cbe-9f9c-ae7af21d401e","resolution":{"observed_at":"2026-08-14T14:44:50.677530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.662307Z","title":"https://www.kaggle.com/c/diabetic-retinopathy- detection/discussion/15617","venue":null,"work_id":"325759d4-3785-40e5-986e-209eec52f5c9","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.016749Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:ec96a36ef97b300c2dd5d51db895b788106380094c9af6225fa995e6a28ad9ec","observation_id":"d9083d38-4b17-48df-9a58-1f06193c4ea2","resolution":{"observed_at":"2026-08-14T14:44:50.666361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.650092Z","title":"Fong and Andrea Vedaldi","venue":null,"work_id":"02fd3ed2-bfa8-40ca-b385-46f1669b2832","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.020710Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:7050cb3a16ea799442e2529fba2702f4a80a1612cd238b6e73244f8e01ccb836","observation_id":"a77597cf-f817-4866-a047-77156407c1c8","resolution":{"observed_at":"2026-08-14T14:44:50.654659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.638434Z","title":"Gondal, Jan M","venue":null,"work_id":"fda826d1-b7f0-422e-81e0-cb0903a9dc68","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.023937Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:58c1166624c996adf055237056343e621fd9eab9109639bfd2e6f652beebc1b6","observation_id":"18e853d2-fbd5-43fe-acd8-f9052fa0efd9","resolution":{"observed_at":"2026-08-14T14:44:50.642159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.027596Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.027596Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:c798b997fd382d4c83fd633d111ee179c2ef5a5adfd341872212cf81e4c31095","observation_id":"5ebe6ca3-56e4-4dcb-b452-229d22be032f","resolution":{"observed_at":"2026-08-14T14:44:50.027596Z","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":"2026-08-14T14:44:50.617361Z","title":"Development and validation of a deep learning algo- rithm for detection of diabetic retinopathy in retinal fundus photographs","venue":null,"work_id":"28bfa984-e225-43c8-9909-08702954dc66","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.032338Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:cd6b38a040c3a509df127d2c031bf7eb773daa46293f787b3758d7062b14ef29","observation_id":"aa960ebe-5b9d-4a11-94e1-efc762b9ba7b","resolution":{"observed_at":"2026-08-14T14:44:50.621500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.00737","last_updated":"2015-05-04T18:05:52Z","snapshot_observed_at":"2026-08-14T22:49:55.891664Z","submitted_at":"2015-05-04T18:05:52Z","title":"A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction","version":1},"cited_work":{"arxiv_id":"1505.00737","doi":null,"metadata_source":"pith","pith_arxiv_id":"1505.00737","snapshot_observed_at":"2026-08-14T14:44:50.277270Z","title":"A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction","venue":"cs.CV","work_id":"db208dbe-5e96-4d77-932e-d61032fab76a","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.037253Z"},"links":{"cited_paper":"/paper/1505.00737","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:b5d46eb9c5be2092e99e2dcf040d730e3e01e697f0b606e9698d9557a4ff9aad","observation_id":"3d04d77b-6e67-4e92-a99e-db263a2e2768","resolution":{"observed_at":"2026-08-14T14:44:50.282296Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.605428Z","title":"Learning both weights and connections for efﬁcient neural network","venue":null,"work_id":"71668c53-9e01-4f42-ab5e-fd8334a3ea44","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.041225Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:8135cf787454cbf7a22dd070f223d8ad2148ea18fe7ef08158f2b7535c8067a3","observation_id":"390c5dd0-6adf-44b5-8295-e5722eaedab8","resolution":{"observed_at":"2026-08-14T14:44:50.609667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.593598Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"48fb44c1-f53d-4789-ab57-43b57187042e","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.044755Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:a9747177bdb9612a91e21bfcd8db674085d5382a715f8ad94eae666bffdd94d0","observation_id":"3b53f3b2-a08d-42fa-9152-7b019dbdc602","resolution":{"observed_at":"2026-08-14T14:44:50.597609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-16T18:00:58.008096Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-14T14:44:50.048056Z","title":"Distilling the knowledge in a neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.048056Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:68a76336566b474132982119787f923251ba20bfa5fc33d85267b837a4928487","observation_id":"1c730fd2-cf0f-4c60-bf1a-a59f5b761ce8","resolution":{"observed_at":"2026-08-14T14:44:50.048056Z","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":"2026-08-14T14:44:50.582187Z","title":"The DIARETDB1 diabetic retinopathy database and evaluation protocol","venue":null,"work_id":"c661d52f-fa40-4d32-a95a-4aea0f6ada1b","year":2007},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.051947Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:0274a441e1504d68e2e5f209e6f32090f426f2db2ca21194638c57c1fdda8597","observation_id":"36152dd5-f876-49d9-aeb4-d3b80767f2b7","resolution":{"observed_at":"2026-08-14T14:44:50.586129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.570963Z","title":"ImageNet classiﬁcation with deep convolutional neural net- works","venue":null,"work_id":"7402f190-9dea-4aaf-a516-a49f27c98d3e","year":2012},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.055919Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:963675fecebe94c51b75bc0bf8e92a02d7f06854440b117c2e119b54f5b4a7e6","observation_id":"8474de84-41f6-4596-a206-7e3029dccc38","resolution":{"observed_at":"2026-08-14T14:44:50.574718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.02533","last_updated":"2017-02-11T00:39:39Z","snapshot_observed_at":"2026-08-16T03:14:49.760924Z","submitted_at":"2016-07-08T21:12:11Z","title":"Adversarial examples in the physical world","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.02533","snapshot_observed_at":"2026-08-14T14:44:50.059950Z","title":"Adver- sarial examples in the physical world","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.059950Z"},"links":{"cited_paper":"/paper/1607.02533","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:a34fdb7745251f6fa4c03d83f3f4e4b7c70baffaa05786aceb03d78f6a249702","observation_id":"defc7247-2e0c-4c8a-a66d-e4ea59fc3fa9","resolution":{"observed_at":"2026-08-14T14:44:50.059950Z","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":"2026-08-14T14:44:50.560069Z","title":"A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images","venue":null,"work_id":"8c552cf5-5653-489a-be91-a55d7c5016e0","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.064334Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:acf9b73dba2006afccc4df84f8d1f260726164c087d07ff7bcd1a3a07e7bac9b","observation_id":"e91243c9-cb56-48aa-a7b2-db28a56ba35b","resolution":{"observed_at":"2026-08-14T14:44:50.564105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.548625Z","title":"De- tection of red lesions in diabetic retinopathy affected fundus images","venue":null,"work_id":"5616348d-4772-4ed2-90f5-d47cddfe801c","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.068348Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:3181cfd47e878578d86fdd65991d1a90908369af9dd7943f5a978ad61b58c529","observation_id":"ae895a81-771c-4faf-8741-a3486e18d98d","resolution":{"observed_at":"2026-08-14T14:44:50.552677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.537135Z","title":"Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning","venue":null,"work_id":"4e357f31-c920-49fa-9fdc-21239efc869c","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.073071Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:02e8876d68687af6648ace8fa7fd8993cfd3af85b1a1ee95eab145630d0b4148","observation_id":"ec3aeb0d-188c-46cb-8e36-a909f7777c6a","resolution":{"observed_at":"2026-08-14T14:44:50.540983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.07039","last_updated":"2020-02-13T16:23:39Z","snapshot_observed_at":"2026-08-14T19:13:59.472513Z","submitted_at":"2018-05-18T03:45:06Z","title":"A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations","version":4},"cited_work":{"arxiv_id":"1805.07039","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.07039","snapshot_observed_at":"2026-08-14T14:44:50.242270Z","title":"A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations","venue":"cs.CV","work_id":"7747475d-2428-474e-a75e-6b6c89f6103b","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.076878Z"},"links":{"cited_paper":"/paper/1805.07039","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:ef8e53b869ccccaf21a56a3c8e568b6a54c68983404bad3cbd9191c9153c476f","observation_id":"85cb1dc7-73ca-41bf-b422-7e30a5aa2049","resolution":{"observed_at":"2026-08-14T14:44:50.246338Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.525502Z","title":"Rise: Random- ized input sampling for explanation of black-box models","venue":null,"work_id":"524dae66-858f-45b4-b596-3ba96a1fce68","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.080896Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:fe6f841415f1914c79f5aebc5fac92a764451e9d22e26e102be03e3f46d8db56","observation_id":"e1ee5708-803f-431d-8648-f0d5f2ff21ba","resolution":{"observed_at":"2026-08-14T14:44:50.529673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.514567Z","title":"Why should I trust you?: Explaining the predictions of any classiﬁer","venue":null,"work_id":"27ba438c-cc00-4beb-9d54-573af87316de","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.084910Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:69a48302de3dc354b0035a6fc361e18fca402506e41b4df51ed945b1917f3212","observation_id":"ebef4813-0054-4571-b796-ac3f66ddbea5","resolution":{"observed_at":"2026-08-14T14:44:50.518385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.503535Z","title":"Berg, and Li Fei-Fei","venue":null,"work_id":"485b5ce0-7a2e-4785-9972-07c5d843c457","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.089018Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:c103f3e9cf3c66f682bb0b2b57ad44874d798931d0f8d1808d3ff9fc32805e59","observation_id":"8292487c-2f06-4b31-a58a-e51f6a722d4a","resolution":{"observed_at":"2026-08-14T14:44:50.507364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.491019Z","title":"Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra","venue":null,"work_id":"2bf893d3-6507-4d73-a0e8-14617c407962","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.092856Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:d3ed68e82214e5ae2f9c6ae048fe45508fdc96131d5447e3af598dc9ae1e0086","observation_id":"a5358405-ee9f-4231-a745-f5b475fad765","resolution":{"observed_at":"2026-08-14T14:44:50.495451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.11720","last_updated":"2018-08-01T09:01:35Z","snapshot_observed_at":"2026-08-14T18:46:03.225248Z","submitted_at":"2018-07-31T09:37:39Z","title":"Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks","version":2},"cited_work":{"arxiv_id":"1807.11720","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.11720","snapshot_observed_at":"2026-08-14T14:44:50.224060Z","title":"Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks","venue":"cs.CV","work_id":"eea6d2d7-f5c6-479d-a4bf-dc05e4b6c1e4","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.096235Z"},"links":{"cited_paper":"/paper/1807.11720","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:4d4e686b9a01f55996fae2fe03a1e5cd63cf2ec887960ed61404e6241efca123","observation_id":"2953658c-ef20-4c50-9a78-d2317998710f","resolution":{"observed_at":"2026-08-14T14:44:50.231007Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.479506Z","title":"Learning important features through propagating activation differences","venue":null,"work_id":"00cf36a1-bcb7-487d-8b81-8b410a98bd4d","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.100268Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:3f2997aa46be9395b5c13caefe8ac1e6c3e5ff436014c04b13434f236b584f5f","observation_id":"7207bc28-7ea5-4413-ab0c-0d7947a93674","resolution":{"observed_at":"2026-08-14T14:44:50.483549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.466947Z","title":"Deep inside convolutional networks: Visualising image clas- siﬁcation models and saliency maps","venue":null,"work_id":"3f5638c9-737b-48b7-8c8d-9901672ba7b9","year":2014},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.103640Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:202f19759a25ad87f6f124c07bb8ecf703d78bb8f23c5920fb423bfb3aa9b344","observation_id":"67d360df-8ef2-440e-89f5-59798d84443c","resolution":{"observed_at":"2026-08-14T14:44:50.471504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-14T14:44:50.107144Z","title":"Very deep convo- lutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.107144Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:75c89679035b8f0e2e48f1d8661648210400ed91dc91f21f9064c66710e21171","observation_id":"dde99103-8d64-445c-83e0-e514348b237a","resolution":{"observed_at":"2026-08-14T14:44:50.107144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03825","last_updated":"2017-06-12T19:53:30Z","snapshot_observed_at":"2026-08-14T20:54:30.978618Z","submitted_at":"2017-06-12T19:53:30Z","title":"SmoothGrad: removing noise by adding noise","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03825","snapshot_observed_at":"2026-08-14T14:44:50.110687Z","title":"Smoothgrad: removing noise by adding noise","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.110687Z"},"links":{"cited_paper":"/paper/1706.03825","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:747eeb878403e89339b50ad608a2239812027b93e391a526cf5db28575bad34f","observation_id":"5bbae37b-25f5-4e26-a20e-f1e3dcb96a4c","resolution":{"observed_at":"2026-08-14T14:44:50.110687Z","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":"2026-08-14T14:44:50.455649Z","title":"Solomon, Emily Chew, Elia J","venue":null,"work_id":"08df565f-33cc-4e05-a915-c1abd5c877cb","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.114299Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:60e946269f627d26d641551be6e89d4007ad3a0e7a49575bdf1afe8f336a8417","observation_id":"0577fe7d-7337-477f-aab8-a81d1fe9e86b","resolution":{"observed_at":"2026-08-14T14:44:50.459506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.442986Z","title":"Striving for simplicity: The all convolutional net","venue":null,"work_id":"4257921c-ace7-43fc-ab75-c733cc4e8474","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.117909Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:5b3013e57b9c0487df6acbfd53ea04c017dac21a6df6b7d60f77e792f970bad1","observation_id":"d6ddc946-8312-4cec-85a1-1a494b62d0ff","resolution":{"observed_at":"2026-08-14T14:44:50.447999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.431467Z","title":"Axiomatic attribution for deep networks","venue":null,"work_id":"73d11840-80e7-4562-92e8-134966664878","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.122034Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:9f65d297985c712cf2afac21e8ae0ad5861997da28f338bd9ea9290bad2cc830","observation_id":"9daebb06-6d90-467e-8230-568ff1215b80","resolution":{"observed_at":"2026-08-14T14:44:50.435400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.418961Z","title":"Going deeper with convolutions","venue":null,"work_id":"99f460d7-8472-415e-863a-809b3927d01d","year":2015},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.125172Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:c213b81410c34b5b928f14086885ca14ec82e5815539940e23f968d5eb60734d","observation_id":"eedf79ac-7086-4f09-8c36-2ebd62c0304a","resolution":{"observed_at":"2026-08-14T14:44:50.423338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.407416Z","title":"In- triguing properties of neural networks","venue":null,"work_id":"09d7671a-f123-4d1e-bcce-c0719319a8f1","year":2014},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.129349Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:7d2452e3f9ad0dc9cb9e260ca1c9e641bfcab2a0e8a7859f80dd7dfbf6a41d7c","observation_id":"fbe8d15a-b7b0-4a6f-9675-9251af3a24bb","resolution":{"observed_at":"2026-08-14T14:44:50.411810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.395308Z","title":"Development and validation of a deep learn- ing system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with dia- betes","venue":null,"work_id":"707f7aaf-6706-43f3-adec-c78c96cc28fe","year":2017},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.133442Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:18f69c391bb3b88b0065f7b483b483e412b4f9e67d830fd6f61b348be6083305","observation_id":"90ba53d6-a622-4726-b212-34e59e61027b","resolution":{"observed_at":"2026-08-14T14:44:50.399146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.383761Z","title":"Rogers, Ryo Kawasaki, Ecosse L","venue":null,"work_id":"d75a5d6f-97b4-4575-9b4d-b6ecada493d2","year":2012},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.136807Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:a5ae5cfa7db52c1843cf0b0c2acb1064cc35d5afb32772e927fb1d3a9c9ee00a","observation_id":"57ea6f3d-6625-4911-b32f-c9108ba2b314","resolution":{"observed_at":"2026-08-14T14:44:50.387716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.368932Z","title":"Zeiler and Rob Fergus","venue":null,"work_id":"a0fbb919-98f7-4650-804e-4406a39d5802","year":2014},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.140335Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:48f84ba2e4d5c212ab465b68ec42962a9c473d291cb7fd54e7a73be59f35306b","observation_id":"91205c0d-c74a-4129-ac11-5fc14b48a59c","resolution":{"observed_at":"2026-08-14T14:44:50.373784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.354551Z","title":"Top-down neural attention by excitation backprop","venue":null,"work_id":"bd23c85d-dca9-40be-8791-d057e0fb19d3","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.143807Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:619df7adeea2644847af967e2bd37cef387446c26ddc6705c9f82f4617f7bfd5","observation_id":"5992d10f-97ee-44c6-9b3c-d02b13fcb1b3","resolution":{"observed_at":"2026-08-14T14:44:50.359892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.342984Z","title":"Visual interpretability for deep learning: A survey","venue":null,"work_id":"e9fd561a-b0a0-4070-ba33-5d6e41555473","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.147265Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:64754e8254d05cacd7462c1b1f7448a33ca4b35bf0d844632392ee1d9ce854de","observation_id":"e2bc5f23-0152-4b76-8ac7-6994c43d3aeb","resolution":{"observed_at":"2026-08-14T14:44:50.346856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.331228Z","title":"Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases","venue":null,"work_id":"5acbd73f-78b6-4b4f-81d0-e128f6d151c8","year":2018},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.150620Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:769076e1634dc5ccaa02f472016477f7eb5d0ed024d5b5c9a48333bc00a3cbd2","observation_id":"635846c8-aca7-412b-932a-487ddcea8484","resolution":{"observed_at":"2026-08-14T14:44:50.335261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6856","last_updated":"2015-04-15T19:06:41Z","snapshot_observed_at":"2026-08-16T21:36:31.797355Z","submitted_at":"2014-12-22T01:14:01Z","title":"Object Detectors Emerge in Deep Scene CNNs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6856","snapshot_observed_at":"2026-08-14T14:44:50.154553Z","title":"Object Detectors Emerge in Deep Scene CNNs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.154553Z"},"links":{"cited_paper":"/paper/1412.6856","citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:fdd5cb72ab6bd0cb3ff1c80662cc66e0df5e4d7cdff916fa4df826bc309d9d9f","observation_id":"eeba07b5-a613-4444-a54d-f839ff65d6bf","resolution":{"observed_at":"2026-08-14T14:44:50.154553Z","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":"2026-08-14T14:44:50.321045Z","title":"Learning deep features for discrimi- native localization","venue":null,"work_id":"9bfb06f9-1363-4ce6-a567-429f9fa8e539","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.158375Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:246ffcbe0b2e744fa0bd17b883827871fd31a270edae29e4138c66a2cc760692","observation_id":"5959cca3-cc36-4b52-acda-6201a9652a3a","resolution":{"observed_at":"2026-08-14T14:44:50.324469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:44:50.310076Z","title":"Au- tomatic hemorrhage detection in color fundus images based on gradual removal of vascular branches","venue":null,"work_id":"a5cefcc0-0ecc-4594-8cd8-1622ec82ec53","year":2016},"citing_paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T14:44:50.161834Z"},"links":{"citing_paper":"/paper/1908.02686"},"observation_digest":"sha256:5c03c429e370d5c97ba6fa1b320beabaca810abe3dfa0c2079125d983ef54b84","observation_id":"ef7c5f0e-2e2d-4824-b674-b08f82856898","resolution":{"observed_at":"2026-08-14T14:44:50.313726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.02686","last_updated":"2019-08-07T15:39:55Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T03:15:56.575691Z","submitted_at":"2019-08-07T15:39:55Z","title":"Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":3,"verified_fuzzy":43},"total_outbound_references":54},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:1908.02686."}