{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:PBVFVINBFIQRKSFPHXCRA3MVMM","short_pith_number":"pith:PBVFVINB","canonical_record":{"source":{"id":"2103.05152","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-09T00:25:34Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"66f1f82442993579f174c84ac69e6daf0fd85da7fa6e0ecda420fa3c1f8f6510","abstract_canon_sha256":"13274761150614464d637243bfa8c95c8c829148afdad59498974d17ee0fd008"},"schema_version":"1.0"},"canonical_sha256":"786a5aa1a12a211548af3dc5106d9563118f105c36c6662710247de464802110","source":{"kind":"arxiv","id":"2103.05152","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05152","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05152v1","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05152","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_12","alias_value":"PBVFVINBFIQR","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_16","alias_value":"PBVFVINBFIQRKSFP","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_8","alias_value":"PBVFVINB","created_at":"2026-07-05T02:21:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:PBVFVINBFIQRKSFPHXCRA3MVMM","target":"record","payload":{"canonical_record":{"source":{"id":"2103.05152","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-09T00:25:34Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"66f1f82442993579f174c84ac69e6daf0fd85da7fa6e0ecda420fa3c1f8f6510","abstract_canon_sha256":"13274761150614464d637243bfa8c95c8c829148afdad59498974d17ee0fd008"},"schema_version":"1.0"},"canonical_sha256":"786a5aa1a12a211548af3dc5106d9563118f105c36c6662710247de464802110","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:21:23.053755Z","signature_b64":"1tGKEsoSY2cTIUAYqym89b2zpkjS7Y3NYj6qtQxfskGGHJJS7Laqnm7ZM6X0DEqV3wJb9ymwrJyAj0D2LJWYBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"786a5aa1a12a211548af3dc5106d9563118f105c36c6662710247de464802110","last_reissued_at":"2026-07-05T02:21:23.053363Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:21:23.053363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.05152","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":"2026-07-05T02:21:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0zogs64CSLKeGxGPfUXn2L8MzpdgQpzWW5mePAKNxdPuwMJ2vET5nrbk49G8s4maYbW67JG8W50wycLcUu3DAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:10:07.189112Z"},"content_sha256":"250b730c81bb9c73f94cd05da6b076b89dc2424c7093c1efb8f537c1910f366b","schema_version":"1.0","event_id":"sha256:250b730c81bb9c73f94cd05da6b076b89dc2424c7093c1efb8f537c1910f366b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:PBVFVINBFIQRKSFPHXCRA3MVMM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge Evolution in Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abhinav Shrivastava, Ahmed Taha, Larry Davis","submitted_at":"2021-03-09T00:25:34Z","abstract_excerpt":"Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively small dataset? To tackle this question, we propose an evolution-inspired training approach to boost performance on relatively small datasets. The knowledge evolution (KE) approach splits a deep network into two hypotheses: the fit-hypothesis and the reset-hypothesis. We iteratively evolve the knowledge inside the fit-hypothesis by perturbing the reset-hypothesis for multiple generations. This approach not only boosts"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05152","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/2103.05152/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":"2026-07-05T02:21:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TWXrpafUAEFxhlvK7f/7p65MRABdgpv2mjwlrBTgSxAagM+PfGjEp6fy+c58OPYveDVMzzXZ2OBCzroJAc/LAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:10:07.189632Z"},"content_sha256":"ca50ea8f55ac56cb1e01245d27d506968a7ea60c32d27e33fc25226081591919","schema_version":"1.0","event_id":"sha256:ca50ea8f55ac56cb1e01245d27d506968a7ea60c32d27e33fc25226081591919"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/bundle.json","state_url":"https://pith.science/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/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":"2026-08-07T17:10:07Z","links":{"resolver":"https://pith.science/pith/PBVFVINBFIQRKSFPHXCRA3MVMM","bundle":"https://pith.science/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/bundle.json","state":"https://pith.science/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PBVFVINBFIQRKSFPHXCRA3MVMM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PBVFVINBFIQRKSFPHXCRA3MVMM","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":"13274761150614464d637243bfa8c95c8c829148afdad59498974d17ee0fd008","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-09T00:25:34Z","title_canon_sha256":"66f1f82442993579f174c84ac69e6daf0fd85da7fa6e0ecda420fa3c1f8f6510"},"schema_version":"1.0","source":{"id":"2103.05152","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05152","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05152v1","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05152","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_12","alias_value":"PBVFVINBFIQR","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_16","alias_value":"PBVFVINBFIQRKSFP","created_at":"2026-07-05T02:21:23Z"},{"alias_kind":"pith_short_8","alias_value":"PBVFVINB","created_at":"2026-07-05T02:21:23Z"}],"graph_snapshots":[{"event_id":"sha256:ca50ea8f55ac56cb1e01245d27d506968a7ea60c32d27e33fc25226081591919","target":"graph","created_at":"2026-07-05T02:21:23Z","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/2103.05152/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively small dataset? To tackle this question, we propose an evolution-inspired training approach to boost performance on relatively small datasets. The knowledge evolution (KE) approach splits a deep network into two hypotheses: the fit-hypothesis and the reset-hypothesis. We iteratively evolve the knowledge inside the fit-hypothesis by perturbing the reset-hypothesis for multiple generations. This approach not only boosts","authors_text":"Abhinav Shrivastava, Ahmed Taha, Larry Davis","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-09T00:25:34Z","title":"Knowledge Evolution in Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05152","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":"sha256:250b730c81bb9c73f94cd05da6b076b89dc2424c7093c1efb8f537c1910f366b","target":"record","created_at":"2026-07-05T02:21:23Z","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":"13274761150614464d637243bfa8c95c8c829148afdad59498974d17ee0fd008","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-09T00:25:34Z","title_canon_sha256":"66f1f82442993579f174c84ac69e6daf0fd85da7fa6e0ecda420fa3c1f8f6510"},"schema_version":"1.0","source":{"id":"2103.05152","kind":"arxiv","version":1}},"canonical_sha256":"786a5aa1a12a211548af3dc5106d9563118f105c36c6662710247de464802110","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"786a5aa1a12a211548af3dc5106d9563118f105c36c6662710247de464802110","first_computed_at":"2026-07-05T02:21:23.053363Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:21:23.053363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1tGKEsoSY2cTIUAYqym89b2zpkjS7Y3NYj6qtQxfskGGHJJS7Laqnm7ZM6X0DEqV3wJb9ymwrJyAj0D2LJWYBw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:21:23.053755Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.05152","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:250b730c81bb9c73f94cd05da6b076b89dc2424c7093c1efb8f537c1910f366b","sha256:ca50ea8f55ac56cb1e01245d27d506968a7ea60c32d27e33fc25226081591919"],"state_sha256":"3909b55086e05df951db99946de641553b8cf165544d8699eb03012d9e248b84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xukiN24+pG/6JXT2y4GaTZSh1mFTG3IKV+BXSCZ1+4kJCm8LrUAIgkpzOdgjkb6SqwWDk/brEo0Nufoa2a5pBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:10:07.196223Z","bundle_sha256":"3c5ed7e9bb08c6336ff5950fa660bb197eddfc38b72a0bfd2e8c790113c3781e"}}