{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SSGPTP6UGQIM3NV57NUSISHP2D","short_pith_number":"pith:SSGPTP6U","schema_version":"1.0","canonical_sha256":"948cf9bfd43410cdb6bdfb692448efd0d61934d626c1da1c40368c7bd088ed7c","source":{"kind":"arxiv","id":"1807.08024","version":3},"attestation_state":"computed","paper":{"title":"Explaining Image Classifiers by Counterfactual Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Goldenberg, Chun-Hao Chang, David Duvenaud, Elliot Creager","submitted_at":"2018-07-20T20:48:44Z","abstract_excerpt":"When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision? Producing an answer requires marginalizing over images that could have been seen but weren't. We can sample plausible image in-fills by conditioning a generative model on the rest of the image. We then optimize to find the image regions that most change the classifier's decision after in-fill. Our approach contrasts with ad-hoc in-filling approaches, such as blurring o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1807.08024","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-20T20:48:44Z","cross_cats_sorted":[],"title_canon_sha256":"977a043d9145aee2b7871ce463b7f28889e5e5680e6568ff35c84719b7165560","abstract_canon_sha256":"6e402eb92f4dad5fe88342fa7b8c393d92fa4c7c656170179523f25e0e925dba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:45.427834Z","signature_b64":"Ik6bgaCLlbYGe3YE014Et0dFCLsmETqMfE57wrUZ04l1YfWTVnqxYfVC6NPIAHkz0Iwj4ro+oyKLJKAemOr2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"948cf9bfd43410cdb6bdfb692448efd0d61934d626c1da1c40368c7bd088ed7c","last_reissued_at":"2026-05-17T23:52:45.427230Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:45.427230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explaining Image Classifiers by Counterfactual Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Goldenberg, Chun-Hao Chang, David Duvenaud, Elliot Creager","submitted_at":"2018-07-20T20:48:44Z","abstract_excerpt":"When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision? Producing an answer requires marginalizing over images that could have been seen but weren't. We can sample plausible image in-fills by conditioning a generative model on the rest of the image. We then optimize to find the image regions that most change the classifier's decision after in-fill. Our approach contrasts with ad-hoc in-filling approaches, such as blurring o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.08024","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.08024","created_at":"2026-05-17T23:52:45.427323+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.08024v3","created_at":"2026-05-17T23:52:45.427323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.08024","created_at":"2026-05-17T23:52:45.427323+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSGPTP6UGQIM","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSGPTP6UGQIM3NV5","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSGPTP6U","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09799","citing_title":"Explainable Human Activity Recognition: A Unified Review of Concepts and Mechanisms","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D","json":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D.json","graph_json":"https://pith.science/api/pith-number/SSGPTP6UGQIM3NV57NUSISHP2D/graph.json","events_json":"https://pith.science/api/pith-number/SSGPTP6UGQIM3NV57NUSISHP2D/events.json","paper":"https://pith.science/paper/SSGPTP6U"},"agent_actions":{"view_html":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D","download_json":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D.json","view_paper":"https://pith.science/paper/SSGPTP6U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.08024&json=true","fetch_graph":"https://pith.science/api/pith-number/SSGPTP6UGQIM3NV57NUSISHP2D/graph.json","fetch_events":"https://pith.science/api/pith-number/SSGPTP6UGQIM3NV57NUSISHP2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D/action/storage_attestation","attest_author":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D/action/author_attestation","sign_citation":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D/action/citation_signature","submit_replication":"https://pith.science/pith/SSGPTP6UGQIM3NV57NUSISHP2D/action/replication_record"}},"created_at":"2026-05-17T23:52:45.427323+00:00","updated_at":"2026-05-17T23:52:45.427323+00:00"}