{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3PULRSW6T3NY2K6EXG7U73T4LY","short_pith_number":"pith:3PULRSW6","canonical_record":{"source":{"id":"2309.07698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-14T13:17:02Z","cross_cats_sorted":[],"title_canon_sha256":"fd88de7b169fb072aa096a118b473a5153d5a794fcfb129f5bd19b1eb24ce835","abstract_canon_sha256":"e0ea7aceebab7deb6dd3fed23bec82501559c86d8166fb9b29d38f4cec2d9e6b"},"schema_version":"1.0"},"canonical_sha256":"dbe8b8cade9edb8d2bc4b9bf4fee7c5e0be02b58138a62d5e444a50409171b64","source":{"kind":"arxiv","id":"2309.07698","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.07698","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"arxiv_version","alias_value":"2309.07698v1","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07698","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_12","alias_value":"3PULRSW6T3NY","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_16","alias_value":"3PULRSW6T3NY2K6E","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_8","alias_value":"3PULRSW6","created_at":"2026-07-05T06:50:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3PULRSW6T3NY2K6EXG7U73T4LY","target":"record","payload":{"canonical_record":{"source":{"id":"2309.07698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-14T13:17:02Z","cross_cats_sorted":[],"title_canon_sha256":"fd88de7b169fb072aa096a118b473a5153d5a794fcfb129f5bd19b1eb24ce835","abstract_canon_sha256":"e0ea7aceebab7deb6dd3fed23bec82501559c86d8166fb9b29d38f4cec2d9e6b"},"schema_version":"1.0"},"canonical_sha256":"dbe8b8cade9edb8d2bc4b9bf4fee7c5e0be02b58138a62d5e444a50409171b64","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:46.733824Z","signature_b64":"xIIdMcBNpVsKAbH+xcoBbAn78pdemcRVxiMq0GfjpoR2MqMGa32POSPEgw940++nV0Ehj12vqx1Romuce1vdDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbe8b8cade9edb8d2bc4b9bf4fee7c5e0be02b58138a62d5e444a50409171b64","last_reissued_at":"2026-07-05T06:50:46.733362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:46.733362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.07698","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":"2026-07-05T06:50:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v102m7gIxBr/kgYA86pq1eoZ+41vCDOUrF+T615dEM65S1C0fe6bp48/4sYMMezI1inDxqg9L684EbV4YtaTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T04:37:18.719440Z"},"content_sha256":"b93733017ebcbd160cc49be2989d60858d48c42788c75ad8f59d314906e09dcc","schema_version":"1.0","event_id":"sha256:b93733017ebcbd160cc49be2989d60858d48c42788c75ad8f59d314906e09dcc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3PULRSW6T3NY2K6EXG7U73T4LY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dataset Condensation via Generative Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuhui Xue, David Junhao Zhang, Heng Wang, Mike Zheng Shou, Rui Yan, Song Bai, Wenqing Zhang","submitted_at":"2023-09-14T13:17:02Z","abstract_excerpt":"Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However, it suffers from slow optimization speed and large number of parameters to be optimized. When increasing image resolutions and classes, the number of learnable parameters grows accordingly, prohibiting condensation methods from scaling up to large datasets with diverse classes. Moreover, the relations among condensed samples have been neglected and hence the feature distribution of condensed samples is often not div"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07698","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/2309.07698/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":"2026-07-05T06:50:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/1rmmD8hnhvauvvn/RbmUfUBrRlrNuElASQK15jL/PE083qRpRT9SUq/SVSx/RMeXN6V/wb/OIm1TD1bzbQjBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T04:37:18.719933Z"},"content_sha256":"0d72af62484644adb92269373aa59ff25ac22372c8f3f3cc1f920773b13ff371","schema_version":"1.0","event_id":"sha256:0d72af62484644adb92269373aa59ff25ac22372c8f3f3cc1f920773b13ff371"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3PULRSW6T3NY2K6EXG7U73T4LY/bundle.json","state_url":"https://pith.science/pith/3PULRSW6T3NY2K6EXG7U73T4LY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3PULRSW6T3NY2K6EXG7U73T4LY/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":"2026-08-21T04:37:18Z","links":{"resolver":"https://pith.science/pith/3PULRSW6T3NY2K6EXG7U73T4LY","bundle":"https://pith.science/pith/3PULRSW6T3NY2K6EXG7U73T4LY/bundle.json","state":"https://pith.science/pith/3PULRSW6T3NY2K6EXG7U73T4LY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3PULRSW6T3NY2K6EXG7U73T4LY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3PULRSW6T3NY2K6EXG7U73T4LY","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":"e0ea7aceebab7deb6dd3fed23bec82501559c86d8166fb9b29d38f4cec2d9e6b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-14T13:17:02Z","title_canon_sha256":"fd88de7b169fb072aa096a118b473a5153d5a794fcfb129f5bd19b1eb24ce835"},"schema_version":"1.0","source":{"id":"2309.07698","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.07698","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"arxiv_version","alias_value":"2309.07698v1","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07698","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_12","alias_value":"3PULRSW6T3NY","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_16","alias_value":"3PULRSW6T3NY2K6E","created_at":"2026-07-05T06:50:46Z"},{"alias_kind":"pith_short_8","alias_value":"3PULRSW6","created_at":"2026-07-05T06:50:46Z"}],"graph_snapshots":[{"event_id":"sha256:0d72af62484644adb92269373aa59ff25ac22372c8f3f3cc1f920773b13ff371","target":"graph","created_at":"2026-07-05T06:50:46Z","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/2309.07698/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However, it suffers from slow optimization speed and large number of parameters to be optimized. When increasing image resolutions and classes, the number of learnable parameters grows accordingly, prohibiting condensation methods from scaling up to large datasets with diverse classes. Moreover, the relations among condensed samples have been neglected and hence the feature distribution of condensed samples is often not div","authors_text":"Chuhui Xue, David Junhao Zhang, Heng Wang, Mike Zheng Shou, Rui Yan, Song Bai, Wenqing Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-14T13:17:02Z","title":"Dataset Condensation via Generative Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07698","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:b93733017ebcbd160cc49be2989d60858d48c42788c75ad8f59d314906e09dcc","target":"record","created_at":"2026-07-05T06:50:46Z","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":"e0ea7aceebab7deb6dd3fed23bec82501559c86d8166fb9b29d38f4cec2d9e6b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-14T13:17:02Z","title_canon_sha256":"fd88de7b169fb072aa096a118b473a5153d5a794fcfb129f5bd19b1eb24ce835"},"schema_version":"1.0","source":{"id":"2309.07698","kind":"arxiv","version":1}},"canonical_sha256":"dbe8b8cade9edb8d2bc4b9bf4fee7c5e0be02b58138a62d5e444a50409171b64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbe8b8cade9edb8d2bc4b9bf4fee7c5e0be02b58138a62d5e444a50409171b64","first_computed_at":"2026-07-05T06:50:46.733362Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:46.733362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xIIdMcBNpVsKAbH+xcoBbAn78pdemcRVxiMq0GfjpoR2MqMGa32POSPEgw940++nV0Ehj12vqx1Romuce1vdDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:46.733824Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.07698","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b93733017ebcbd160cc49be2989d60858d48c42788c75ad8f59d314906e09dcc","sha256:0d72af62484644adb92269373aa59ff25ac22372c8f3f3cc1f920773b13ff371"],"state_sha256":"ba823431d7d56238b990c4a0a0b10f6b7e4ed4cf34ebc25acc01c53e7ade915f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nlRQ8Uu/zMBPdJnpB0mLX8dvvgJ2XYGG1+v/CV8Fdxyu4q6pViBYcnYevhZtzkpcdGkfLIij9pf1BgN4NPhpCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T04:37:18.724932Z","bundle_sha256":"c67f9a8b2f0b3ba7fc9850a6eb26558a3da54352af8c76ad52cd2d83ee9983d3"}}