{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5HUC42JJSDPQ7JIMT2T6H5R4HD","short_pith_number":"pith:5HUC42JJ","canonical_record":{"source":{"id":"2403.19941","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-29T02:49:15Z","cross_cats_sorted":[],"title_canon_sha256":"446a52231241808788161d711538a7218ac9640dd453553dc221e2cb34858b6a","abstract_canon_sha256":"5a8ed532364fb39cebe1c8ab9b3ee994a15a54c6796d730bf49b7e2a65b90e22"},"schema_version":"1.0"},"canonical_sha256":"e9e82e692990df0fa50c9ea7e3f63c38dd84e10737a21b0a6ce227cb05abefff","source":{"kind":"arxiv","id":"2403.19941","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19941","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19941v1","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19941","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_12","alias_value":"5HUC42JJSDPQ","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_16","alias_value":"5HUC42JJSDPQ7JIM","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_8","alias_value":"5HUC42JJ","created_at":"2026-07-05T08:02:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5HUC42JJSDPQ7JIMT2T6H5R4HD","target":"record","payload":{"canonical_record":{"source":{"id":"2403.19941","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-29T02:49:15Z","cross_cats_sorted":[],"title_canon_sha256":"446a52231241808788161d711538a7218ac9640dd453553dc221e2cb34858b6a","abstract_canon_sha256":"5a8ed532364fb39cebe1c8ab9b3ee994a15a54c6796d730bf49b7e2a65b90e22"},"schema_version":"1.0"},"canonical_sha256":"e9e82e692990df0fa50c9ea7e3f63c38dd84e10737a21b0a6ce227cb05abefff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:10.888243Z","signature_b64":"yZGUQ1BRqrDijwmnlWiVz4Fxc4DMZ5mc+gBiZT0eMUexMu/LarRY16pxskkg2gqKObvJPohpDxWfL7fCkH1dCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9e82e692990df0fa50c9ea7e3f63c38dd84e10737a21b0a6ce227cb05abefff","last_reissued_at":"2026-07-05T08:02:10.887818Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:10.887818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.19941","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:02:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4DWBOp0oL1Zd1oQ7CHezsYinLHdFuNnxyD3XCnqQRBBjaVe26b/Z4ZZykWxjgCVqqByq7+nY5HBDcLfIVfc4BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:38:12.678329Z"},"content_sha256":"3fbf936828b41fd187e3c1450c3186e8efe8c892dd3167d7a51ab60a593fcea4","schema_version":"1.0","event_id":"sha256:3fbf936828b41fd187e3c1450c3186e8efe8c892dd3167d7a51ab60a593fcea4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5HUC42JJSDPQ7JIMT2T6H5R4HD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diverse Feature Learning by Self-distillation and Reset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Sejik Park","submitted_at":"2024-03-29T02:49:15Z","abstract_excerpt":"Our paper addresses the problem of models struggling to learn diverse features, due to either forgetting previously learned features or failing to learn new ones. To overcome this problem, we introduce Diverse Feature Learning (DFL), a method that combines an important feature preservation algorithm with a new feature learning algorithm. Specifically, for preserving important features, we utilize self-distillation in ensemble models by selecting the meaningful model weights observed during training. For learning new features, we employ reset that involves periodically re-initializing part of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19941","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/2403.19941/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":"2026-07-05T08:02:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UZWHwziNmaW9HtzdzpiPKdjlU40BpRxwu0r1XA0aGgpVdipcl63Sl7rEaEPmNs7v7waWRGGZxpnnqJBTsQ2rAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:38:12.680849Z"},"content_sha256":"6507e3b1f9f7765c2b0c81887c63f9e88c239ecdbddaaf1098be7ceace0a4084","schema_version":"1.0","event_id":"sha256:6507e3b1f9f7765c2b0c81887c63f9e88c239ecdbddaaf1098be7ceace0a4084"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/bundle.json","state_url":"https://pith.science/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/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":"2026-08-13T20:38:12Z","links":{"resolver":"https://pith.science/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD","bundle":"https://pith.science/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/bundle.json","state":"https://pith.science/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5HUC42JJSDPQ7JIMT2T6H5R4HD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5HUC42JJSDPQ7JIMT2T6H5R4HD","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":"5a8ed532364fb39cebe1c8ab9b3ee994a15a54c6796d730bf49b7e2a65b90e22","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-29T02:49:15Z","title_canon_sha256":"446a52231241808788161d711538a7218ac9640dd453553dc221e2cb34858b6a"},"schema_version":"1.0","source":{"id":"2403.19941","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19941","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19941v1","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19941","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_12","alias_value":"5HUC42JJSDPQ","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_16","alias_value":"5HUC42JJSDPQ7JIM","created_at":"2026-07-05T08:02:10Z"},{"alias_kind":"pith_short_8","alias_value":"5HUC42JJ","created_at":"2026-07-05T08:02:10Z"}],"graph_snapshots":[{"event_id":"sha256:6507e3b1f9f7765c2b0c81887c63f9e88c239ecdbddaaf1098be7ceace0a4084","target":"graph","created_at":"2026-07-05T08:02:10Z","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/2403.19941/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Our paper addresses the problem of models struggling to learn diverse features, due to either forgetting previously learned features or failing to learn new ones. To overcome this problem, we introduce Diverse Feature Learning (DFL), a method that combines an important feature preservation algorithm with a new feature learning algorithm. Specifically, for preserving important features, we utilize self-distillation in ensemble models by selecting the meaningful model weights observed during training. For learning new features, we employ reset that involves periodically re-initializing part of t","authors_text":"Sejik Park","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-29T02:49:15Z","title":"Diverse Feature Learning by Self-distillation and Reset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19941","kind":"arxiv","version":1},"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":"sha256:3fbf936828b41fd187e3c1450c3186e8efe8c892dd3167d7a51ab60a593fcea4","target":"record","created_at":"2026-07-05T08:02:10Z","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":"5a8ed532364fb39cebe1c8ab9b3ee994a15a54c6796d730bf49b7e2a65b90e22","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-29T02:49:15Z","title_canon_sha256":"446a52231241808788161d711538a7218ac9640dd453553dc221e2cb34858b6a"},"schema_version":"1.0","source":{"id":"2403.19941","kind":"arxiv","version":1}},"canonical_sha256":"e9e82e692990df0fa50c9ea7e3f63c38dd84e10737a21b0a6ce227cb05abefff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9e82e692990df0fa50c9ea7e3f63c38dd84e10737a21b0a6ce227cb05abefff","first_computed_at":"2026-07-05T08:02:10.887818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:10.887818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yZGUQ1BRqrDijwmnlWiVz4Fxc4DMZ5mc+gBiZT0eMUexMu/LarRY16pxskkg2gqKObvJPohpDxWfL7fCkH1dCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:10.888243Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19941","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fbf936828b41fd187e3c1450c3186e8efe8c892dd3167d7a51ab60a593fcea4","sha256:6507e3b1f9f7765c2b0c81887c63f9e88c239ecdbddaaf1098be7ceace0a4084"],"state_sha256":"f7f2d4ad1ffe2ad08970d7ea04744baafd0eaee662c5f62a1bf40e1df306f27b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rzXFZXoY2sR8W3CSAkXS/iYrsJFTqPz2/C9TJ5ZGi/U4nJwvRZRCw8PwtMMDIFKMNuSHnMOLOLnWxL1bvmrfAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T20:38:12.694598Z","bundle_sha256":"d5f2997e5698c0923cf90c426eda2cb01a72eb82b9d24ca1a0374087667db427"}}