{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:B2SU7LNQU6IIOJBQASQAVWYZVL","short_pith_number":"pith:B2SU7LNQ","schema_version":"1.0","canonical_sha256":"0ea54fadb0a79087243004a00adb19aac0d44e882aafbbc6a7abf49ce3178aa1","source":{"kind":"arxiv","id":"2408.08201","version":1},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.08201","created_at":"2026-07-05T08:55:45.853943+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.08201v1","created_at":"2026-07-05T08:55:45.853943+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08201","created_at":"2026-07-05T08:55:45.853943+00:00"},{"alias_kind":"pith_short_12","alias_value":"B2SU7LNQU6II","created_at":"2026-07-05T08:55:45.853943+00:00"},{"alias_kind":"pith_short_16","alias_value":"B2SU7LNQU6IIOJBQ","created_at":"2026-07-05T08:55:45.853943+00:00"},{"alias_kind":"pith_short_8","alias_value":"B2SU7LNQ","created_at":"2026-07-05T08:55:45.853943+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL","json":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL.json","graph_json":"https://pith.science/api/pith-number/B2SU7LNQU6IIOJBQASQAVWYZVL/graph.json","events_json":"https://pith.science/api/pith-number/B2SU7LNQU6IIOJBQASQAVWYZVL/events.json","paper":"https://pith.science/paper/B2SU7LNQ"},"agent_actions":{"view_html":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL","download_json":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL.json","view_paper":"https://pith.science/paper/B2SU7LNQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.08201&json=true","fetch_graph":"https://pith.science/api/pith-number/B2SU7LNQU6IIOJBQASQAVWYZVL/graph.json","fetch_events":"https://pith.science/api/pith-number/B2SU7LNQU6IIOJBQASQAVWYZVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/action/storage_attestation","attest_author":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/action/author_attestation","sign_citation":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/action/citation_signature","submit_replication":"https://pith.science/pith/B2SU7LNQU6IIOJBQASQAVWYZVL/action/replication_record"}},"created_at":"2026-07-05T08:55:45.853943+00:00","updated_at":"2026-07-05T08:55:45.853943+00:00"}