{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VL4GXCBAHJLMB72ATLEXCIXFU6","short_pith_number":"pith:VL4GXCBA","canonical_record":{"source":{"id":"1809.00193","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-01T14:24:36Z","cross_cats_sorted":[],"title_canon_sha256":"062e8700ad80fb39a08f091bef58b7d4ac752efe0468702e62fc534795808671","abstract_canon_sha256":"c7e311db04f64f465a265ffa826558a83e4167a0e0877c65a5bab2efefab223f"},"schema_version":"1.0"},"canonical_sha256":"aaf86b88203a56c0ff409ac97122e5a7b834fb360fb0ca062caa5766ddfed42f","source":{"kind":"arxiv","id":"1809.00193","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00193","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00193v2","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00193","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"VL4GXCBAHJLM","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VL4GXCBAHJLMB72A","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VL4GXCBA","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VL4GXCBAHJLMB72ATLEXCIXFU6","target":"record","payload":{"canonical_record":{"source":{"id":"1809.00193","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-01T14:24:36Z","cross_cats_sorted":[],"title_canon_sha256":"062e8700ad80fb39a08f091bef58b7d4ac752efe0468702e62fc534795808671","abstract_canon_sha256":"c7e311db04f64f465a265ffa826558a83e4167a0e0877c65a5bab2efefab223f"},"schema_version":"1.0"},"canonical_sha256":"aaf86b88203a56c0ff409ac97122e5a7b834fb360fb0ca062caa5766ddfed42f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:19.919994Z","signature_b64":"QrHwPrdVLEL+AjD6HUok2TR7V9o0LMJ/CKbVaI1dvVHw2d4e/22PtSMFsB7XjQW8bt+QUk6XGt/7wDvWpb0uDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaf86b88203a56c0ff409ac97122e5a7b834fb360fb0ca062caa5766ddfed42f","last_reissued_at":"2026-05-18T00:06:19.919431Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:19.919431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.00193","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HExykZfF1XHSaFknwwkqMhP8K00DYeK+DJgQYFgIicnPSWLWtgUlmHkyN83KZvu3wlzUSG/T6/1/+KpPHeriBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:18:04.845788Z"},"content_sha256":"b0c756a54f8b404fd136ec828676e560cae2fa7bf5401f67a5e691791a54f42e","schema_version":"1.0","event_id":"sha256:b0c756a54f8b404fd136ec828676e560cae2fa7bf5401f67a5e691791a54f42e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VL4GXCBAHJLMB72ATLEXCIXFU6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data Dropout: Optimizing Training Data for Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Li, Jun Huan, Tianyang Wang","submitted_at":"2018-09-01T14:24:36Z","abstract_excerpt":"Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00193","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lGn3JaJhpUUobUoDQVuinB35ttSIjDCGljlSstbYvDun8cqUhmYucUZYD2md82PV0OrsdTbuiFXVkPJcDjWSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:18:04.846164Z"},"content_sha256":"4c4dc3c3f46d67a04fb2b63fe6e3a263957c6a5b67401f41ae83f9c1458a8ff3","schema_version":"1.0","event_id":"sha256:4c4dc3c3f46d67a04fb2b63fe6e3a263957c6a5b67401f41ae83f9c1458a8ff3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/bundle.json","state_url":"https://pith.science/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/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":"2026-05-25T14:18:04Z","links":{"resolver":"https://pith.science/pith/VL4GXCBAHJLMB72ATLEXCIXFU6","bundle":"https://pith.science/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/bundle.json","state":"https://pith.science/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VL4GXCBAHJLMB72ATLEXCIXFU6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VL4GXCBAHJLMB72ATLEXCIXFU6","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":"c7e311db04f64f465a265ffa826558a83e4167a0e0877c65a5bab2efefab223f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-01T14:24:36Z","title_canon_sha256":"062e8700ad80fb39a08f091bef58b7d4ac752efe0468702e62fc534795808671"},"schema_version":"1.0","source":{"id":"1809.00193","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00193","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00193v2","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00193","created_at":"2026-05-18T00:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"VL4GXCBAHJLM","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VL4GXCBAHJLMB72A","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VL4GXCBA","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:4c4dc3c3f46d67a04fb2b63fe6e3a263957c6a5b67401f41ae83f9c1458a8ff3","target":"graph","created_at":"2026-05-18T00:06:19Z","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"},"paper":{"abstract_excerpt":"Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training","authors_text":"Bo Li, Jun Huan, Tianyang Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-01T14:24:36Z","title":"Data Dropout: Optimizing Training Data for Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00193","kind":"arxiv","version":2},"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:b0c756a54f8b404fd136ec828676e560cae2fa7bf5401f67a5e691791a54f42e","target":"record","created_at":"2026-05-18T00:06:19Z","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":"c7e311db04f64f465a265ffa826558a83e4167a0e0877c65a5bab2efefab223f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-01T14:24:36Z","title_canon_sha256":"062e8700ad80fb39a08f091bef58b7d4ac752efe0468702e62fc534795808671"},"schema_version":"1.0","source":{"id":"1809.00193","kind":"arxiv","version":2}},"canonical_sha256":"aaf86b88203a56c0ff409ac97122e5a7b834fb360fb0ca062caa5766ddfed42f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aaf86b88203a56c0ff409ac97122e5a7b834fb360fb0ca062caa5766ddfed42f","first_computed_at":"2026-05-18T00:06:19.919431Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:19.919431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QrHwPrdVLEL+AjD6HUok2TR7V9o0LMJ/CKbVaI1dvVHw2d4e/22PtSMFsB7XjQW8bt+QUk6XGt/7wDvWpb0uDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:19.919994Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00193","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0c756a54f8b404fd136ec828676e560cae2fa7bf5401f67a5e691791a54f42e","sha256:4c4dc3c3f46d67a04fb2b63fe6e3a263957c6a5b67401f41ae83f9c1458a8ff3"],"state_sha256":"7d1673d11cc3be04364a4f932020c7d94023cd96b483f181872839b81db74c97"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yp1n55djNpNgn62Wn2YipqXRoDi4srTkTrKdAorQtlMJfwqV4V2SEFN4MHLAvCrzbsOnm9DaX7BEmd58EqD5CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T14:18:04.849668Z","bundle_sha256":"87384c9f4251f63f0a88030a04ef00efb319776b9333c5439f746886ae4c97d5"}}