{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:P4UKCOPEOMTR7QFCCGGYGTOA3I","short_pith_number":"pith:P4UKCOPE","canonical_record":{"source":{"id":"2303.14666","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-26T09:40:55Z","cross_cats_sorted":[],"title_canon_sha256":"e6f333f33987d51cbbd41941f1f9e6092417cdf3cfe1c8d788c8dfc13ce94cc8","abstract_canon_sha256":"414be121a9191ad6c3de8de043cabdfad523ed1a84341e043b29b7cd69e88de5"},"schema_version":"1.0"},"canonical_sha256":"7f28a139e473271fc0a2118d834dc0da16c492044c58c2d4dfb5ecba59426b3f","source":{"kind":"arxiv","id":"2303.14666","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.14666","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"arxiv_version","alias_value":"2303.14666v1","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.14666","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_12","alias_value":"P4UKCOPEOMTR","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_16","alias_value":"P4UKCOPEOMTR7QFC","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_8","alias_value":"P4UKCOPE","created_at":"2026-07-05T05:54:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:P4UKCOPEOMTR7QFCCGGYGTOA3I","target":"record","payload":{"canonical_record":{"source":{"id":"2303.14666","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-26T09:40:55Z","cross_cats_sorted":[],"title_canon_sha256":"e6f333f33987d51cbbd41941f1f9e6092417cdf3cfe1c8d788c8dfc13ce94cc8","abstract_canon_sha256":"414be121a9191ad6c3de8de043cabdfad523ed1a84341e043b29b7cd69e88de5"},"schema_version":"1.0"},"canonical_sha256":"7f28a139e473271fc0a2118d834dc0da16c492044c58c2d4dfb5ecba59426b3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:44.784514Z","signature_b64":"lab+yF/MT0+91aC2AgyRIuCJlok7iS51QdVom9MUXcWTlUJ9Nmck7PPJb6l05lJuKe88rLKtFp4BSyzzv1w7Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f28a139e473271fc0a2118d834dc0da16c492044c58c2d4dfb5ecba59426b3f","last_reissued_at":"2026-07-05T05:54:44.784012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:44.784012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.14666","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":"2026-07-05T05:54:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o5MTXvY55vfGeNp3bAmlfIv1UsE328TVfKIaoqYNUEEELJjz1ud+XqEj4yNWbL/AvgayK2tci7bykjil4EOACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:26:46.729532Z"},"content_sha256":"af4646aa89072d4406baac8ecc806635af56e19db7afec9b3fc98388be5e39cf","schema_version":"1.0","event_id":"sha256:af4646aa89072d4406baac8ecc806635af56e19db7afec9b3fc98388be5e39cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:P4UKCOPEOMTR7QFCCGGYGTOA3I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haofei Zhang, Jiangtao Zhang, Jie Song, Lechao Cheng, Mengqi Xue, Mingli Song, Tianli Zhang, Yu Wang","submitted_at":"2023-03-26T09:40:55Z","abstract_excerpt":"Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally, model generalization can be reflected in the flatness of the loss landscape. Since averaging parameters of multiple models can find flatter minima, we are inspired to extend the process to the sampled convex combinations of multi-student mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.14666","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/2303.14666/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":"2026-07-05T05:54:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cJXGRh47WrUuBx3AEqCyihO1wrkDN5do17XS+vOy7INr3Z07/vcO2U45JHd1uVdqGvhiY4tSHtvvYwt5vOV9Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:26:46.729907Z"},"content_sha256":"54bca46622bd0098280287e895b92980d6b296ad60504c6e19eb1534e25e9c1e","schema_version":"1.0","event_id":"sha256:54bca46622bd0098280287e895b92980d6b296ad60504c6e19eb1534e25e9c1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/bundle.json","state_url":"https://pith.science/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/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":"2026-07-18T11:26:46Z","links":{"resolver":"https://pith.science/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I","bundle":"https://pith.science/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/bundle.json","state":"https://pith.science/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P4UKCOPEOMTR7QFCCGGYGTOA3I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:P4UKCOPEOMTR7QFCCGGYGTOA3I","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":"414be121a9191ad6c3de8de043cabdfad523ed1a84341e043b29b7cd69e88de5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-26T09:40:55Z","title_canon_sha256":"e6f333f33987d51cbbd41941f1f9e6092417cdf3cfe1c8d788c8dfc13ce94cc8"},"schema_version":"1.0","source":{"id":"2303.14666","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.14666","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"arxiv_version","alias_value":"2303.14666v1","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.14666","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_12","alias_value":"P4UKCOPEOMTR","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_16","alias_value":"P4UKCOPEOMTR7QFC","created_at":"2026-07-05T05:54:44Z"},{"alias_kind":"pith_short_8","alias_value":"P4UKCOPE","created_at":"2026-07-05T05:54:44Z"}],"graph_snapshots":[{"event_id":"sha256:54bca46622bd0098280287e895b92980d6b296ad60504c6e19eb1534e25e9c1e","target":"graph","created_at":"2026-07-05T05:54:44Z","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/2303.14666/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally, model generalization can be reflected in the flatness of the loss landscape. Since averaging parameters of multiple models can find flatter minima, we are inspired to extend the process to the sampled convex combinations of multi-student mo","authors_text":"Haofei Zhang, Jiangtao Zhang, Jie Song, Lechao Cheng, Mengqi Xue, Mingli Song, Tianli Zhang, Yu Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-26T09:40:55Z","title":"Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.14666","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:af4646aa89072d4406baac8ecc806635af56e19db7afec9b3fc98388be5e39cf","target":"record","created_at":"2026-07-05T05:54:44Z","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":"414be121a9191ad6c3de8de043cabdfad523ed1a84341e043b29b7cd69e88de5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-26T09:40:55Z","title_canon_sha256":"e6f333f33987d51cbbd41941f1f9e6092417cdf3cfe1c8d788c8dfc13ce94cc8"},"schema_version":"1.0","source":{"id":"2303.14666","kind":"arxiv","version":1}},"canonical_sha256":"7f28a139e473271fc0a2118d834dc0da16c492044c58c2d4dfb5ecba59426b3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f28a139e473271fc0a2118d834dc0da16c492044c58c2d4dfb5ecba59426b3f","first_computed_at":"2026-07-05T05:54:44.784012Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:54:44.784012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lab+yF/MT0+91aC2AgyRIuCJlok7iS51QdVom9MUXcWTlUJ9Nmck7PPJb6l05lJuKe88rLKtFp4BSyzzv1w7Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:54:44.784514Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.14666","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af4646aa89072d4406baac8ecc806635af56e19db7afec9b3fc98388be5e39cf","sha256:54bca46622bd0098280287e895b92980d6b296ad60504c6e19eb1534e25e9c1e"],"state_sha256":"9f595a2a6911c4d18bd8f7ab298ed0224aa85cb32c5789f282a193d4ba2bc08e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZKgFI8BpOofSvQNr61+lNk4enL6H7GUwnx02zmKirp5SlmtkUlQ1ISYzIBdZET69voFfjHnSK/o03ZMdELaRAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T11:26:46.732055Z","bundle_sha256":"2aec8ca5f589ef9043c45ff684baac63bc68011250e2385adb6638a125cc63fd"}}