{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YTTKBR3ZTUCCHBMAW5G2MB7AQS","short_pith_number":"pith:YTTKBR3Z","canonical_record":{"source":{"id":"2312.11973","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T09:11:49Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9418b62a71e18f555956fd2175e18f5c34d5ead688e16223895df2a09ccb9286","abstract_canon_sha256":"e948577873fced8c39cb6d4bc53366974ef260d748f4255db3a410b091051d3e"},"schema_version":"1.0"},"canonical_sha256":"c4e6a0c7799d04238580b74da607e084b115086ddfbb598579a251f918f28ee5","source":{"kind":"arxiv","id":"2312.11973","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.11973","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"arxiv_version","alias_value":"2312.11973v6","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11973","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_12","alias_value":"YTTKBR3ZTUCC","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_16","alias_value":"YTTKBR3ZTUCCHBMA","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_8","alias_value":"YTTKBR3Z","created_at":"2026-07-05T09:51:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YTTKBR3ZTUCCHBMAW5G2MB7AQS","target":"record","payload":{"canonical_record":{"source":{"id":"2312.11973","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T09:11:49Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9418b62a71e18f555956fd2175e18f5c34d5ead688e16223895df2a09ccb9286","abstract_canon_sha256":"e948577873fced8c39cb6d4bc53366974ef260d748f4255db3a410b091051d3e"},"schema_version":"1.0"},"canonical_sha256":"c4e6a0c7799d04238580b74da607e084b115086ddfbb598579a251f918f28ee5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:30.058797Z","signature_b64":"ylfq2EZffkuV4uJRa+1nodnKQnkQ6s7nTCMXXNJ0uQtB72zSJW2U6G7Hb4Z5ivSXGzMNR1andHmG3onAjjG2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4e6a0c7799d04238580b74da607e084b115086ddfbb598579a251f918f28ee5","last_reissued_at":"2026-07-05T09:51:30.058348Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:30.058348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.11973","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:51:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kNxjzx9aVAfdDLbzGngj3y2+lGOqVlcxJMvo0bYVr1zOu8PMZSOQ9KrLNX9A2//nlHi2o9JV2NaV/xOgW2RDAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:50:31.270842Z"},"content_sha256":"083027cf206563066c7cca47b69d2073f29311513cbce93792033ecc6d9ba28f","schema_version":"1.0","event_id":"sha256:083027cf206563066c7cca47b69d2073f29311513cbce93792033ecc6d9ba28f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YTTKBR3ZTUCCHBMAW5G2MB7AQS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continual Learning: Forget-free Winning Subnetworks for Video Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chang D. Yoo, Haeyong Kang, Jaehong Yoon, Sung Ju Hwang","submitted_at":"2023-12-19T09:11:49Z","abstract_excerpt":"Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the existence of efficient subnetworks within larger, dense networks, a high-performing Winning Subnetwork (WSN) in terms of task performance under appropriate sparsity conditions is considered for various continual learning tasks. It leverages pre-existing weights from dense networks to achieve efficient learning in Task Incremental Learning (TIL) and Task-agnostic Incremental Learning (TaIL) scenarios. In Few-Shot Class Incremental Learning (FSCIL), a variation of WSN referred to as the Soft subnetwork (SoftNet) is designed to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11973","kind":"arxiv","version":6},"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/2312.11973/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:51:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"APgKTO//x0xNcZ25ViPVnlLyFN/QL8xuNVzI1PbTOcqa0JNssJmAg/hHcvcpW0zEAtZzo2Ua03IdtiIwOwDXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:50:31.271350Z"},"content_sha256":"838db3d0c52e77d8d6a1ad0d41ac77a457a834ded5b57188a996e3257960681d","schema_version":"1.0","event_id":"sha256:838db3d0c52e77d8d6a1ad0d41ac77a457a834ded5b57188a996e3257960681d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/bundle.json","state_url":"https://pith.science/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T02:50:31Z","links":{"resolver":"https://pith.science/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS","bundle":"https://pith.science/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/bundle.json","state":"https://pith.science/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YTTKBR3ZTUCCHBMAW5G2MB7AQS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YTTKBR3ZTUCCHBMAW5G2MB7AQS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e948577873fced8c39cb6d4bc53366974ef260d748f4255db3a410b091051d3e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T09:11:49Z","title_canon_sha256":"9418b62a71e18f555956fd2175e18f5c34d5ead688e16223895df2a09ccb9286"},"schema_version":"1.0","source":{"id":"2312.11973","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.11973","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"arxiv_version","alias_value":"2312.11973v6","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11973","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_12","alias_value":"YTTKBR3ZTUCC","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_16","alias_value":"YTTKBR3ZTUCCHBMA","created_at":"2026-07-05T09:51:30Z"},{"alias_kind":"pith_short_8","alias_value":"YTTKBR3Z","created_at":"2026-07-05T09:51:30Z"}],"graph_snapshots":[{"event_id":"sha256:838db3d0c52e77d8d6a1ad0d41ac77a457a834ded5b57188a996e3257960681d","target":"graph","created_at":"2026-07-05T09:51:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2312.11973/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the existence of efficient subnetworks within larger, dense networks, a high-performing Winning Subnetwork (WSN) in terms of task performance under appropriate sparsity conditions is considered for various continual learning tasks. It leverages pre-existing weights from dense networks to achieve efficient learning in Task Incremental Learning (TIL) and Task-agnostic Incremental Learning (TaIL) scenarios. In Few-Shot Class Incremental Learning (FSCIL), a variation of WSN referred to as the Soft subnetwork (SoftNet) is designed to","authors_text":"Chang D. Yoo, Haeyong Kang, Jaehong Yoon, Sung Ju Hwang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T09:11:49Z","title":"Continual Learning: Forget-free Winning Subnetworks for Video Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11973","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:083027cf206563066c7cca47b69d2073f29311513cbce93792033ecc6d9ba28f","target":"record","created_at":"2026-07-05T09:51:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e948577873fced8c39cb6d4bc53366974ef260d748f4255db3a410b091051d3e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T09:11:49Z","title_canon_sha256":"9418b62a71e18f555956fd2175e18f5c34d5ead688e16223895df2a09ccb9286"},"schema_version":"1.0","source":{"id":"2312.11973","kind":"arxiv","version":6}},"canonical_sha256":"c4e6a0c7799d04238580b74da607e084b115086ddfbb598579a251f918f28ee5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4e6a0c7799d04238580b74da607e084b115086ddfbb598579a251f918f28ee5","first_computed_at":"2026-07-05T09:51:30.058348Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:30.058348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ylfq2EZffkuV4uJRa+1nodnKQnkQ6s7nTCMXXNJ0uQtB72zSJW2U6G7Hb4Z5ivSXGzMNR1andHmG3onAjjG2CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:30.058797Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.11973","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:083027cf206563066c7cca47b69d2073f29311513cbce93792033ecc6d9ba28f","sha256:838db3d0c52e77d8d6a1ad0d41ac77a457a834ded5b57188a996e3257960681d"],"state_sha256":"a1bf32772192f65c6211fc143bc6c61563647d6f23de7cf3ba42e0b47bc0469d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eqZMfQjxPNaRjEMqvbS3PE6my4pYZL6UpAv9x7G7FEENxSUUY+RTSDXMiKXHq86DxGOimefrPS4aufHl2pzoAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T02:50:31.276124Z","bundle_sha256":"92ef0a2bef5360dfb256f006ab8ab8f6d845b27c4f024014f9b3a2947a28ba8e"}}