{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:G6JXKYFRZWOQVHX7HG24ZNNVSZ","short_pith_number":"pith:G6JXKYFR","schema_version":"1.0","canonical_sha256":"37937560b1cd9d0a9eff39b5ccb5b5967ce88bdd3211b0dce3aa0c2baf0df2c2","source":{"kind":"arxiv","id":"1811.11082","version":2},"attestation_state":"computed","paper":{"title":"Automatic Face Aging in Videos via Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Nhan Duong, Eric Patterson, Kha Gia Quach, Khoa Luu, Ngan Le, Nghia Nguyen, Tien D. Bui","submitted_at":"2018-11-27T16:41:39Z","abstract_excerpt":"This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning. The proposed method models facial structures and the longitudinal face-aging process of given subjects coherently across video frames. The approach is optimized using a long-term reward, Reinforcement Learning function with deep feature extraction from Deep Convolutional Neural Network. Unlike previous age-progression methods that are only able to synthesize an aged likeness of a face from a single input image, the proposed approach is capable of 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":"1811.11082","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T16:41:39Z","cross_cats_sorted":[],"title_canon_sha256":"32f416cbe6a69f80630fbf238a3ff5cfe59e95e691a4f6a77a2e8f4eb1ecb2cd","abstract_canon_sha256":"d4bfd866b3dc89f40eb9e72bf4b2600eed820cc848be89e67cafed38b315d7f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:50.800251Z","signature_b64":"uWRVb4fvlpbNWNYHhla8999/c402tv+NpDhN7LERNSBBg+qtvyqamvbwW/ckP54iEUJa4ZRQacBkiyyccRInCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37937560b1cd9d0a9eff39b5ccb5b5967ce88bdd3211b0dce3aa0c2baf0df2c2","last_reissued_at":"2026-05-17T23:47:50.799702Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:50.799702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Face Aging in Videos via Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Nhan Duong, Eric Patterson, Kha Gia Quach, Khoa Luu, Ngan Le, Nghia Nguyen, Tien D. Bui","submitted_at":"2018-11-27T16:41:39Z","abstract_excerpt":"This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning. The proposed method models facial structures and the longitudinal face-aging process of given subjects coherently across video frames. The approach is optimized using a long-term reward, Reinforcement Learning function with deep feature extraction from Deep Convolutional Neural Network. Unlike previous age-progression methods that are only able to synthesize an aged likeness of a face from a single input image, the proposed approach is capable of a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11082","kind":"arxiv","version":2},"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":"1811.11082","created_at":"2026-05-17T23:47:50.799785+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.11082v2","created_at":"2026-05-17T23:47:50.799785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11082","created_at":"2026-05-17T23:47:50.799785+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6JXKYFRZWOQ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6JXKYFRZWOQVHX7","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6JXKYFR","created_at":"2026-05-18T12:32:25.280505+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/G6JXKYFRZWOQVHX7HG24ZNNVSZ","json":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ.json","graph_json":"https://pith.science/api/pith-number/G6JXKYFRZWOQVHX7HG24ZNNVSZ/graph.json","events_json":"https://pith.science/api/pith-number/G6JXKYFRZWOQVHX7HG24ZNNVSZ/events.json","paper":"https://pith.science/paper/G6JXKYFR"},"agent_actions":{"view_html":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ","download_json":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ.json","view_paper":"https://pith.science/paper/G6JXKYFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.11082&json=true","fetch_graph":"https://pith.science/api/pith-number/G6JXKYFRZWOQVHX7HG24ZNNVSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/G6JXKYFRZWOQVHX7HG24ZNNVSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ/action/storage_attestation","attest_author":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ/action/author_attestation","sign_citation":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ/action/citation_signature","submit_replication":"https://pith.science/pith/G6JXKYFRZWOQVHX7HG24ZNNVSZ/action/replication_record"}},"created_at":"2026-05-17T23:47:50.799785+00:00","updated_at":"2026-05-17T23:47:50.799785+00:00"}