{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:B2SU7LNQU6IIOJBQASQAVWYZVL","short_pith_number":"pith:B2SU7LNQ","canonical_record":{"source":{"id":"2408.08201","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-15T15:08:58Z","cross_cats_sorted":[],"title_canon_sha256":"a6d8fb35f491cfc666ed2d932a31650bc354af540254824747f9061b8b4aa0f8","abstract_canon_sha256":"c6c3319a39b0e620ce1284d3eb31934c4fc6fbedc5dc08b283812eb524703be6"},"schema_version":"1.0"},"canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","source":{"kind":"arxiv","id":"2408.08201","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08201","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08201v1","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08201","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_12","alias_value":"B2SU7LNQU6II","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_16","alias_value":"B2SU7LNQU6IIOJBQ","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_8","alias_value":"B2SU7LNQ","created_at":"2026-07-05T08:55:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:B2SU7LNQU6IIOJBQASQAVWYZVL","target":"record","payload":{"canonical_record":{"source":{"id":"2408.08201","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-15T15:08:58Z","cross_cats_sorted":[],"title_canon_sha256":"a6d8fb35f491cfc666ed2d932a31650bc354af540254824747f9061b8b4aa0f8","abstract_canon_sha256":"c6c3319a39b0e620ce1284d3eb31934c4fc6fbedc5dc08b283812eb524703be6"},"schema_version":"1.0"},"canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:55:45.854353Z","signature_b64":"/v2DVRbtP2laC2yZRpnNm40LJvFaLGCs2N+xmFoIBuH/5Piwi/qOYh2VoLg7yumtV0gyOkeoHlT7b8Ca4ArVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","last_reissued_at":"2026-07-05T08:55:45.853887Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:55:45.853887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.08201","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":"2026-07-05T08:55:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZYKojJdto6okd5Jxn6+fXCSicC5/ecalwu4CxkaABDEpavZwuniwaD/j/Q+kVsrsnn980XllSqmbs0PSRdU8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:06:23.092311Z"},"content_sha256":"fb944622691787deafa91da5ed2de3a9b10f291ec117410f0458bce9e74b7c6d","schema_version":"1.0","event_id":"sha256:fb944622691787deafa91da5ed2de3a9b10f291ec117410f0458bce9e74b7c6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:B2SU7LNQU6IIOJBQASQAVWYZVL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Heavy Labels Out! Dataset Distillation with Label Space Lightening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingwen Ye, Ruonan Yu, Songhua Liu, Xinchao Wang, Zigeng Chen","submitted_at":"2024-08-15T15:08:58Z","abstract_excerpt":"Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original sets on neural networks are similar. Although the number of training samples can be reduced substantially, current state-of-the-art methods heavily rely on enormous soft labels to achieve satisfactory performance. As a result, the required storage can be comparable even to original datasets, especially for large-scale ones. To solve this problem, instead of storing these heavy labels, we propose a novel label-lighten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08201","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/2408.08201/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":"2026-07-05T08:55:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7PfVn/Uq6Or9bk187cBM0W4Vnya88lLMm5g/tyDvWR20e8uq2J4pf8jygWHaFcwSMtITPNWpCXLbMVv6njZdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:06:23.092647Z"},"content_sha256":"ed25bab76af25a668997938a7e7db89dc9da0b2ff456a1620a8b41c1dad14e84","schema_version":"1.0","event_id":"sha256:ed25bab76af25a668997938a7e7db89dc9da0b2ff456a1620a8b41c1dad14e84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/bundle.json","state_url":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/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":"2026-08-06T19:06:23Z","links":{"resolver":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL","bundle":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/bundle.json","state":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:B2SU7LNQU6IIOJBQASQAVWYZVL","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":"c6c3319a39b0e620ce1284d3eb31934c4fc6fbedc5dc08b283812eb524703be6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-15T15:08:58Z","title_canon_sha256":"a6d8fb35f491cfc666ed2d932a31650bc354af540254824747f9061b8b4aa0f8"},"schema_version":"1.0","source":{"id":"2408.08201","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08201","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08201v1","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08201","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_12","alias_value":"B2SU7LNQU6II","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_16","alias_value":"B2SU7LNQU6IIOJBQ","created_at":"2026-07-05T08:55:45Z"},{"alias_kind":"pith_short_8","alias_value":"B2SU7LNQ","created_at":"2026-07-05T08:55:45Z"}],"graph_snapshots":[{"event_id":"sha256:ed25bab76af25a668997938a7e7db89dc9da0b2ff456a1620a8b41c1dad14e84","target":"graph","created_at":"2026-07-05T08:55:45Z","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/2408.08201/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original sets on neural networks are similar. Although the number of training samples can be reduced substantially, current state-of-the-art methods heavily rely on enormous soft labels to achieve satisfactory performance. As a result, the required storage can be comparable even to original datasets, especially for large-scale ones. To solve this problem, instead of storing these heavy labels, we propose a novel label-lighten","authors_text":"Jingwen Ye, Ruonan Yu, Songhua Liu, Xinchao Wang, Zigeng Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-15T15:08:58Z","title":"Heavy Labels Out! Dataset Distillation with Label Space Lightening"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08201","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:fb944622691787deafa91da5ed2de3a9b10f291ec117410f0458bce9e74b7c6d","target":"record","created_at":"2026-07-05T08:55:45Z","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":"c6c3319a39b0e620ce1284d3eb31934c4fc6fbedc5dc08b283812eb524703be6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-15T15:08:58Z","title_canon_sha256":"a6d8fb35f491cfc666ed2d932a31650bc354af540254824747f9061b8b4aa0f8"},"schema_version":"1.0","source":{"id":"2408.08201","kind":"arxiv","version":1}},"canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","first_computed_at":"2026-07-05T08:55:45.853887Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:55:45.853887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/v2DVRbtP2laC2yZRpnNm40LJvFaLGCs2N+xmFoIBuH/5Piwi/qOYh2VoLg7yumtV0gyOkeoHlT7b8Ca4ArVAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:55:45.854353Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.08201","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb944622691787deafa91da5ed2de3a9b10f291ec117410f0458bce9e74b7c6d","sha256:ed25bab76af25a668997938a7e7db89dc9da0b2ff456a1620a8b41c1dad14e84"],"state_sha256":"4c86cbc8c1f6c697ea747a96afb9759f2322cb6dd803cedb59d4d9d1c3a030db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZIAQVBwlELmS5Uow37utYSoShblVuOb0u4pHWgXnjCRbfqNFnrqJOMux7pT+SaX4z7+UGO58zWMoQRGu1ob1Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T19:06:23.096413Z","bundle_sha256":"f5826793a2f490c7cf9f67bd207ec7d18941a625e842f9aa36394be15871df1b"}}