{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:553RN3Z4V5L2WQEFMXQBFXMAB2","short_pith_number":"pith:553RN3Z4","canonical_record":{"source":{"id":"2301.04272","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-11T02:25:10Z","cross_cats_sorted":["cs.CV","cs.IR"],"title_canon_sha256":"d7934aeed38cc37de7de423e9804fbb9a1e253941547cd84500347d94289060d","abstract_canon_sha256":"71945292840a376450ec637e07f4f755091d91f0c74eaff0a1bebab51924d833"},"schema_version":"1.0"},"canonical_sha256":"ef7716ef3caf57ab408565e012dd800ea52666f80a555e935b4737db5c061a14","source":{"kind":"arxiv","id":"2301.04272","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.04272","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"arxiv_version","alias_value":"2301.04272v2","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.04272","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_12","alias_value":"553RN3Z4V5L2","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_16","alias_value":"553RN3Z4V5L2WQEF","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_8","alias_value":"553RN3Z4","created_at":"2026-07-05T06:54:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:553RN3Z4V5L2WQEFMXQBFXMAB2","target":"record","payload":{"canonical_record":{"source":{"id":"2301.04272","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-11T02:25:10Z","cross_cats_sorted":["cs.CV","cs.IR"],"title_canon_sha256":"d7934aeed38cc37de7de423e9804fbb9a1e253941547cd84500347d94289060d","abstract_canon_sha256":"71945292840a376450ec637e07f4f755091d91f0c74eaff0a1bebab51924d833"},"schema_version":"1.0"},"canonical_sha256":"ef7716ef3caf57ab408565e012dd800ea52666f80a555e935b4737db5c061a14","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:14.222966Z","signature_b64":"L0UE279xkTKFC7J0/mRXpJIAgE3rUYlLYCS2hTFTok55xkp8TAAtXaYgATu7Ii7Jqnvc3V1J/2B1O5AM+FEiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef7716ef3caf57ab408565e012dd800ea52666f80a555e935b4737db5c061a14","last_reissued_at":"2026-07-05T06:54:14.222463Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:14.222463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.04272","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":"2026-07-05T06:54:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ciQQLh0+OZsoNMns1sU7PRs0u6RIJWz+MdZrnX0lNQQGc1uOCcxHRqQO0sl44/+v0U6wkbtE163Pn1nym7bFCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:53:12.883379Z"},"content_sha256":"7e6a1e1de4e68bed00cb04ee2cb13b90aa24f5a8e45eaddf47a15c662c701b31","schema_version":"1.0","event_id":"sha256:7e6a1e1de4e68bed00cb04ee2cb13b90aa24f5a8e45eaddf47a15c662c701b31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:553RN3Z4V5L2WQEFMXQBFXMAB2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data Distillation: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.IR"],"primary_cat":"cs.LG","authors_text":"Julian McAuley, Noveen Sachdeva","submitted_at":"2023-01-11T02:25:10Z","abstract_excerpt":"The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems such as (a) high model-training time; (b) slow research iteration; and (c) poor eco-sustainability. As an alternative, data distillation approaches aim to synthesize terse data summaries, which can serve as effective drop-in replacements of the original dataset for scenarios like model training, inference, architecture search, etc. In this surv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.04272","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2301.04272/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":"2026-07-05T06:54:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MB1Xa5HMPdl43EeC64PVD3yHGx28WbC8IqP8jhtlPY4Rb7HjrEw7PNA00pmS4aEqOdFZ6e5UZwBTSOT4k8NUCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:53:12.884352Z"},"content_sha256":"b61ecdc3c634366c642a19f35c1269a2968cc3cc812dbb16f3181f8c0a456c29","schema_version":"1.0","event_id":"sha256:b61ecdc3c634366c642a19f35c1269a2968cc3cc812dbb16f3181f8c0a456c29"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/bundle.json","state_url":"https://pith.science/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/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":"2026-08-05T14:53:12Z","links":{"resolver":"https://pith.science/pith/553RN3Z4V5L2WQEFMXQBFXMAB2","bundle":"https://pith.science/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/bundle.json","state":"https://pith.science/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/553RN3Z4V5L2WQEFMXQBFXMAB2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:553RN3Z4V5L2WQEFMXQBFXMAB2","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":"71945292840a376450ec637e07f4f755091d91f0c74eaff0a1bebab51924d833","cross_cats_sorted":["cs.CV","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-11T02:25:10Z","title_canon_sha256":"d7934aeed38cc37de7de423e9804fbb9a1e253941547cd84500347d94289060d"},"schema_version":"1.0","source":{"id":"2301.04272","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.04272","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"arxiv_version","alias_value":"2301.04272v2","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.04272","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_12","alias_value":"553RN3Z4V5L2","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_16","alias_value":"553RN3Z4V5L2WQEF","created_at":"2026-07-05T06:54:14Z"},{"alias_kind":"pith_short_8","alias_value":"553RN3Z4","created_at":"2026-07-05T06:54:14Z"}],"graph_snapshots":[{"event_id":"sha256:b61ecdc3c634366c642a19f35c1269a2968cc3cc812dbb16f3181f8c0a456c29","target":"graph","created_at":"2026-07-05T06:54:14Z","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/2301.04272/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems such as (a) high model-training time; (b) slow research iteration; and (c) poor eco-sustainability. As an alternative, data distillation approaches aim to synthesize terse data summaries, which can serve as effective drop-in replacements of the original dataset for scenarios like model training, inference, architecture search, etc. In this surv","authors_text":"Julian McAuley, Noveen Sachdeva","cross_cats":["cs.CV","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-11T02:25:10Z","title":"Data Distillation: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.04272","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":"sha256:7e6a1e1de4e68bed00cb04ee2cb13b90aa24f5a8e45eaddf47a15c662c701b31","target":"record","created_at":"2026-07-05T06:54:14Z","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":"71945292840a376450ec637e07f4f755091d91f0c74eaff0a1bebab51924d833","cross_cats_sorted":["cs.CV","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-11T02:25:10Z","title_canon_sha256":"d7934aeed38cc37de7de423e9804fbb9a1e253941547cd84500347d94289060d"},"schema_version":"1.0","source":{"id":"2301.04272","kind":"arxiv","version":2}},"canonical_sha256":"ef7716ef3caf57ab408565e012dd800ea52666f80a555e935b4737db5c061a14","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef7716ef3caf57ab408565e012dd800ea52666f80a555e935b4737db5c061a14","first_computed_at":"2026-07-05T06:54:14.222463Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:54:14.222463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L0UE279xkTKFC7J0/mRXpJIAgE3rUYlLYCS2hTFTok55xkp8TAAtXaYgATu7Ii7Jqnvc3V1J/2B1O5AM+FEiDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:54:14.222966Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.04272","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e6a1e1de4e68bed00cb04ee2cb13b90aa24f5a8e45eaddf47a15c662c701b31","sha256:b61ecdc3c634366c642a19f35c1269a2968cc3cc812dbb16f3181f8c0a456c29"],"state_sha256":"7727551a71f778ebfcf564d7afc539bc2287921043a4d6c16417dca82d51df3c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XBDwgoCWcbJYnht2bLfkhuBlka5qlMhvK5cZ7ANQ/cU6IhaUA+5A5HTW8FecR6t/hqFKdHWFturwaJcEXovxCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:53:12.899694Z","bundle_sha256":"309f5c6bb8b0b3619612595a006f0462713913e61642d041e8e6e95ad0d7a914"}}