{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:IFTCI2JWY3YUPT4JDAWDYE44DM","short_pith_number":"pith:IFTCI2JW","schema_version":"1.0","canonical_sha256":"4166246936c6f147cf89182c3c139c1b380a30a1c60bd92f362392740bde2f10","source":{"kind":"arxiv","id":"1511.06421","version":3},"attestation_state":"computed","paper":{"title":"Deep Manifold Traversal: Changing Labels with Convolutional Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jacob R. Gardner, John E. Hopcroft, Kavita Bala, Kilian Q. Weinberger, Matt J. Kusner, Paul Upchurch, Yixuan Li","submitted_at":"2015-11-19T22:17:20Z","abstract_excerpt":"Many tasks in computer vision can be cast as a \"label changing\" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to date no general purpose method exists. Motivated by this we propose deep manifold traversal, a method that addresses the problem in its most general form: it first approximates the manifold of natural images then morphs a test image along a traversal path away from a source class a"},"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":"1511.06421","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-19T22:17:20Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"4894b207a598e69c01a00b7dc38f7c0ff4a673ff6c753fe451677a51dfa88a93","abstract_canon_sha256":"699ffdb27cdbc672d6cb20f39edfe254bca64868d76ebc724ed4e28ef294a0ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:54.116508Z","signature_b64":"L3ZwGbQy32av0/vVdPRbncOwcgNc0zPT18RrOAkDJO4dMRl7z+1o6MUPmStgWe6xBfraPuollszqv3PrysIJBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4166246936c6f147cf89182c3c139c1b380a30a1c60bd92f362392740bde2f10","last_reissued_at":"2026-05-18T01:18:54.116039Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:54.116039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Manifold Traversal: Changing Labels with Convolutional Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jacob R. Gardner, John E. Hopcroft, Kavita Bala, Kilian Q. Weinberger, Matt J. Kusner, Paul Upchurch, Yixuan Li","submitted_at":"2015-11-19T22:17:20Z","abstract_excerpt":"Many tasks in computer vision can be cast as a \"label changing\" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to date no general purpose method exists. Motivated by this we propose deep manifold traversal, a method that addresses the problem in its most general form: it first approximates the manifold of natural images then morphs a test image along a traversal path away from a source class a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06421","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":"1511.06421","created_at":"2026-05-18T01:18:54.116102+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.06421v3","created_at":"2026-05-18T01:18:54.116102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06421","created_at":"2026-05-18T01:18:54.116102+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFTCI2JWY3YU","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFTCI2JWY3YUPT4J","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFTCI2JW","created_at":"2026-05-18T12:29:25.134429+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/IFTCI2JWY3YUPT4JDAWDYE44DM","json":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM.json","graph_json":"https://pith.science/api/pith-number/IFTCI2JWY3YUPT4JDAWDYE44DM/graph.json","events_json":"https://pith.science/api/pith-number/IFTCI2JWY3YUPT4JDAWDYE44DM/events.json","paper":"https://pith.science/paper/IFTCI2JW"},"agent_actions":{"view_html":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM","download_json":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM.json","view_paper":"https://pith.science/paper/IFTCI2JW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.06421&json=true","fetch_graph":"https://pith.science/api/pith-number/IFTCI2JWY3YUPT4JDAWDYE44DM/graph.json","fetch_events":"https://pith.science/api/pith-number/IFTCI2JWY3YUPT4JDAWDYE44DM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM/action/storage_attestation","attest_author":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM/action/author_attestation","sign_citation":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM/action/citation_signature","submit_replication":"https://pith.science/pith/IFTCI2JWY3YUPT4JDAWDYE44DM/action/replication_record"}},"created_at":"2026-05-18T01:18:54.116102+00:00","updated_at":"2026-05-18T01:18:54.116102+00:00"}