{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2RWJQ3AADVGGQHLFT2WY2AM3V5","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":"34ab4a7f202e11bd1f0b5d816b24db8c138128761959eb76406ce127d081ed65","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T04:07:14Z","title_canon_sha256":"6544184e032e8543e52124285a01a861a0e091b21fe75806fc18a57ca3e149cf"},"schema_version":"1.0","source":{"id":"2502.08106","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08106","created_at":"2026-07-05T11:21:42Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08106v3","created_at":"2026-07-05T11:21:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08106","created_at":"2026-07-05T11:21:42Z"},{"alias_kind":"pith_short_12","alias_value":"2RWJQ3AADVGG","created_at":"2026-07-05T11:21:42Z"},{"alias_kind":"pith_short_16","alias_value":"2RWJQ3AADVGGQHLF","created_at":"2026-07-05T11:21:42Z"},{"alias_kind":"pith_short_8","alias_value":"2RWJQ3AA","created_at":"2026-07-05T11:21:42Z"}],"graph_snapshots":[{"event_id":"sha256:b67ee99f003279c0939125a2d01c17f604f4ebfafc04def5aa71a8079cc2fad0","target":"graph","created_at":"2026-07-05T11:21:42Z","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/2502.08106/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models have made significant advancements in recent years. However, their performance often deteriorates when trained or fine-tuned on imbalanced datasets. This degradation is largely due to the disproportionate representation of majority and minority data in image-text pairs. In this paper, we propose a general fine-tuning approach, dubbed PoGDiff, to address this challenge. Rather than directly minimizing the KL divergence between the predicted and ground-truth distributions, PoGDiff replaces the ground-truth distribution with a Product of Gaussians (PoG), which is constructed by c","authors_text":"Hao Wang, Sizhe Wei, Xiaoming Huo, Ziyan Wang","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T04:07:14Z","title":"PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08106","kind":"arxiv","version":3},"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:b8cfbf4db510066b26d59fdf344341146120d91cb30521c29f204fdebaccd7d7","target":"record","created_at":"2026-07-05T11:21:42Z","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":"34ab4a7f202e11bd1f0b5d816b24db8c138128761959eb76406ce127d081ed65","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T04:07:14Z","title_canon_sha256":"6544184e032e8543e52124285a01a861a0e091b21fe75806fc18a57ca3e149cf"},"schema_version":"1.0","source":{"id":"2502.08106","kind":"arxiv","version":3}},"canonical_sha256":"d46c986c001d4c681d659ead8d019baf5c1da62f9384d4b552d2ba53b7f2cf03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d46c986c001d4c681d659ead8d019baf5c1da62f9384d4b552d2ba53b7f2cf03","first_computed_at":"2026-07-05T11:21:42.096096Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:42.096096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RR8symkCVi6VQ0gBacWdxiesMTtZnelqLmvsJMzOqRroFVLD3wkECXfFGfddGUsixu8yJeT94Jim/bLaHDe/CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:42.096575Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.08106","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b8cfbf4db510066b26d59fdf344341146120d91cb30521c29f204fdebaccd7d7","sha256:b67ee99f003279c0939125a2d01c17f604f4ebfafc04def5aa71a8079cc2fad0"],"state_sha256":"8d1744f7a8831cff4ed09be199b78f2dcf272d3272d451a0aae56bbb46166166"}