{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:D5KOVCZD6K7XSCVV76OKFZBMAX","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":"11a4c22396bf18ff16365ade1cd60197acdbf33e64d9dfda499ae512af75a957","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-05T14:33:29Z","title_canon_sha256":"162102d68afa6616b180224be31f99ab3db5fccdb10a768e26a30466a821f15b"},"schema_version":"1.0","source":{"id":"2503.04835","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04835","created_at":"2026-07-05T10:25:50Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04835v1","created_at":"2026-07-05T10:25:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04835","created_at":"2026-07-05T10:25:50Z"},{"alias_kind":"pith_short_12","alias_value":"D5KOVCZD6K7X","created_at":"2026-07-05T10:25:50Z"},{"alias_kind":"pith_short_16","alias_value":"D5KOVCZD6K7XSCVV","created_at":"2026-07-05T10:25:50Z"},{"alias_kind":"pith_short_8","alias_value":"D5KOVCZD","created_at":"2026-07-05T10:25:50Z"}],"graph_snapshots":[{"event_id":"sha256:4214fff95837eca2570060e162fb56ef8a1d6b90884fc2eeed329810fd4fcbb5","target":"graph","created_at":"2026-07-05T10:25:50Z","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/2503.04835/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these challenges, dataset distillation has emerged as a promising solution by compressing the large-scale dataset into a smaller synthetic dataset that retains the essential information needed for training. This paper proposes a novel parameterization framework for dataset distillation, coined Distilling Dataset into Neural Field (DDiF), which leverages the neural field to store the necessary information of the large-scal","authors_text":"Donghyeok Shin, Gyuwon Sim, Heesun Bae, Il-Chul Moon, Wanmo Kang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-05T14:33:29Z","title":"Distilling Dataset into Neural Field"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04835","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":"sha256:066edf4c7abea040749ffae796c79504a7f4d9cc6131ce79ee8c07b1d83299d1","target":"record","created_at":"2026-07-05T10:25:50Z","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":"11a4c22396bf18ff16365ade1cd60197acdbf33e64d9dfda499ae512af75a957","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-05T14:33:29Z","title_canon_sha256":"162102d68afa6616b180224be31f99ab3db5fccdb10a768e26a30466a821f15b"},"schema_version":"1.0","source":{"id":"2503.04835","kind":"arxiv","version":1}},"canonical_sha256":"1f54ea8b23f2bf790ab5ff9ca2e42c05ee47e3ea80ca5d0177ffa919f2f7765d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f54ea8b23f2bf790ab5ff9ca2e42c05ee47e3ea80ca5d0177ffa919f2f7765d","first_computed_at":"2026-07-05T10:25:50.155015Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:25:50.155015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EVnf3Dp+CznbdmlFLpdnp4Li3hA7kJKEQO6s/EPUYzYgrawfbddA5b66oM3ODu18WKewLqU2YMeaPnqvyt3iAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:25:50.155895Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04835","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:066edf4c7abea040749ffae796c79504a7f4d9cc6131ce79ee8c07b1d83299d1","sha256:4214fff95837eca2570060e162fb56ef8a1d6b90884fc2eeed329810fd4fcbb5"],"state_sha256":"2ab794c50bc0c458b0becaa6aaa0aeadbe742dffe292173e09a43f629a865d40"}