{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:DFCWQ3ZP72FRBYDPXSCZ7GUULQ","short_pith_number":"pith:DFCWQ3ZP","schema_version":"1.0","canonical_sha256":"1945686f2ffe8b10e06fbc859f9a945c07e55a82c0e49bcf9b98317911f3b9fc","source":{"kind":"arxiv","id":"2203.15064","version":1},"attestation_state":"computed","paper":{"title":"Cycle-Consistent Counterfactuals by Latent Transformations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Li Fuxin, Saeed Khorram","submitted_at":"2022-03-28T20:10:09Z","abstract_excerpt":"CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either require inference-time optimization or joint training with a generative adversarial model which makes them time-consuming and difficult to use in practice. We propose a novel approach, Cycle-Consistent Counterfactuals by Latent Transformations (C3LT), which learns a latent transformation that automatically generates visual CFs by steering in the latent space of generative models. Our method uses cycle consistency betwe"},"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":"2203.15064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-28T20:10:09Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6044461bf683ae3296592e36639194c76e055e4253ae408fd83e32043b2e52b4","abstract_canon_sha256":"699092642d4204e51b595c41b81652cdae1f27db7b966e279511d57d16b3fa62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:09:21.139157Z","signature_b64":"YO78nl7sJoGXAMh3F3WxGGiB8mXLV2fQ+xLvJzPQqZD16isbklYJE0CNesy67Vaa9AjG4wktDtZovv852GEFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1945686f2ffe8b10e06fbc859f9a945c07e55a82c0e49bcf9b98317911f3b9fc","last_reissued_at":"2026-07-05T04:09:21.138516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:09:21.138516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cycle-Consistent Counterfactuals by Latent Transformations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Li Fuxin, Saeed Khorram","submitted_at":"2022-03-28T20:10:09Z","abstract_excerpt":"CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either require inference-time optimization or joint training with a generative adversarial model which makes them time-consuming and difficult to use in practice. We propose a novel approach, Cycle-Consistent Counterfactuals by Latent Transformations (C3LT), which learns a latent transformation that automatically generates visual CFs by steering in the latent space of generative models. Our method uses cycle consistency betwe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15064","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.15064/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2203.15064","created_at":"2026-07-05T04:09:21.138748+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.15064v1","created_at":"2026-07-05T04:09:21.138748+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15064","created_at":"2026-07-05T04:09:21.138748+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFCWQ3ZP72FR","created_at":"2026-07-05T04:09:21.138748+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFCWQ3ZP72FRBYDP","created_at":"2026-07-05T04:09:21.138748+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFCWQ3ZP","created_at":"2026-07-05T04:09:21.138748+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ","json":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ.json","graph_json":"https://pith.science/api/pith-number/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/graph.json","events_json":"https://pith.science/api/pith-number/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/events.json","paper":"https://pith.science/paper/DFCWQ3ZP"},"agent_actions":{"view_html":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ","download_json":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ.json","view_paper":"https://pith.science/paper/DFCWQ3ZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.15064&json=true","fetch_graph":"https://pith.science/api/pith-number/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/graph.json","fetch_events":"https://pith.science/api/pith-number/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/action/storage_attestation","attest_author":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/action/author_attestation","sign_citation":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/action/citation_signature","submit_replication":"https://pith.science/pith/DFCWQ3ZP72FRBYDPXSCZ7GUULQ/action/replication_record"}},"created_at":"2026-07-05T04:09:21.138748+00:00","updated_at":"2026-07-05T04:09:21.138748+00:00"}