{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GPFL54E2QKHXSJX7WYRA3VKHYD","short_pith_number":"pith:GPFL54E2","schema_version":"1.0","canonical_sha256":"33cabef09a828f7926ffb6220dd547c0e654bce6bb4a6868baab0739503287fe","source":{"kind":"arxiv","id":"2606.27696","version":1},"attestation_state":"computed","paper":{"title":"Class-frequency Guided Noise Schedule for Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Beier Zhu, Bei Yu, Hanwang Zhang, Jiequan Cui, Qingshan Xu, Xiaojuan Qi","submitted_at":"2026-06-26T03:43:23Z","abstract_excerpt":"In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, low-density regions often lead to inaccurately estimated scores, thereby compromising the generation quality. Although the multi-scale noise schedule can alleviate this issue during the diffusion process, low-frequency classes still face the challenge of large low-density regions, resulting in more inaccurate estimated scores than high-frequency classes. Furthermore, high-frequency classes tend to dominate the score s"},"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":"2606.27696","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T03:43:23Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f37ea2c1616661d6b35c47adf911d38f00a489bc725045db772dce91848aaf17","abstract_canon_sha256":"17a773fa476944233da094277cb870039fdc588ad7eea772d9d53fedcd5b7b95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:45.678750Z","signature_b64":"VZ3sT86/p32N9bRI6HBTPVPVeviB9+r0zypLVs6yoXco12VFZDItSDQYww5MS4mH63iAsr5UXpUVAG3CHowKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33cabef09a828f7926ffb6220dd547c0e654bce6bb4a6868baab0739503287fe","last_reissued_at":"2026-06-29T01:14:45.678358Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:45.678358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Class-frequency Guided Noise Schedule for Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Beier Zhu, Bei Yu, Hanwang Zhang, Jiequan Cui, Qingshan Xu, Xiaojuan Qi","submitted_at":"2026-06-26T03:43:23Z","abstract_excerpt":"In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, low-density regions often lead to inaccurately estimated scores, thereby compromising the generation quality. Although the multi-scale noise schedule can alleviate this issue during the diffusion process, low-frequency classes still face the challenge of large low-density regions, resulting in more inaccurate estimated scores than high-frequency classes. Furthermore, high-frequency classes tend to dominate the score s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27696","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/2606.27696/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":"2606.27696","created_at":"2026-06-29T01:14:45.678414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27696v1","created_at":"2026-06-29T01:14:45.678414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27696","created_at":"2026-06-29T01:14:45.678414+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPFL54E2QKHX","created_at":"2026-06-29T01:14:45.678414+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPFL54E2QKHXSJX7","created_at":"2026-06-29T01:14:45.678414+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPFL54E2","created_at":"2026-06-29T01:14:45.678414+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/GPFL54E2QKHXSJX7WYRA3VKHYD","json":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD.json","graph_json":"https://pith.science/api/pith-number/GPFL54E2QKHXSJX7WYRA3VKHYD/graph.json","events_json":"https://pith.science/api/pith-number/GPFL54E2QKHXSJX7WYRA3VKHYD/events.json","paper":"https://pith.science/paper/GPFL54E2"},"agent_actions":{"view_html":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD","download_json":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD.json","view_paper":"https://pith.science/paper/GPFL54E2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27696&json=true","fetch_graph":"https://pith.science/api/pith-number/GPFL54E2QKHXSJX7WYRA3VKHYD/graph.json","fetch_events":"https://pith.science/api/pith-number/GPFL54E2QKHXSJX7WYRA3VKHYD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD/action/storage_attestation","attest_author":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD/action/author_attestation","sign_citation":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD/action/citation_signature","submit_replication":"https://pith.science/pith/GPFL54E2QKHXSJX7WYRA3VKHYD/action/replication_record"}},"created_at":"2026-06-29T01:14:45.678414+00:00","updated_at":"2026-06-29T01:14:45.678414+00:00"}