{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3U4WVS2OSCMTUNNLFRORF2RBCN","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":"70ab541ab55bd83be47730cc650fd8f91bcf22e92f3be725854e9e63d6994c75","cross_cats_sorted":["cs.CV","cs.LG","math.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-07-05T10:00:53Z","title_canon_sha256":"818da87e8b3445bd5f5e160dea3c40aafce1c8af3cb0b8b3e0303eb77ed7e127"},"schema_version":"1.0","source":{"id":"2307.02159","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.02159","created_at":"2026-07-05T06:28:05Z"},{"alias_kind":"arxiv_version","alias_value":"2307.02159v1","created_at":"2026-07-05T06:28:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.02159","created_at":"2026-07-05T06:28:05Z"},{"alias_kind":"pith_short_12","alias_value":"3U4WVS2OSCMT","created_at":"2026-07-05T06:28:05Z"},{"alias_kind":"pith_short_16","alias_value":"3U4WVS2OSCMTUNNL","created_at":"2026-07-05T06:28:05Z"},{"alias_kind":"pith_short_8","alias_value":"3U4WVS2O","created_at":"2026-07-05T06:28:05Z"}],"graph_snapshots":[{"event_id":"sha256:e3f591bf60a7e976c6bab7cce4fd0447b7fa3dd043e5b31ec354136463f5bb80","target":"graph","created_at":"2026-07-05T06:28:05Z","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/2307.02159/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs). While these two types of models have shown great success, they suffer from respective limitations that hinder them from achieving fast sampling and high sample quality simultaneously. In this paper, we propose a unified theoretic f","authors_text":"Enze Xie, Han Shi, Jincheng Yu, Jingwei Zhang, Zhenguo Li","cross_cats":["cs.CV","cs.LG","math.AP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-07-05T10:00:53Z","title":"DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.02159","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":"sha256:3c2217cb51096c157eb339db2f2410a7d359a2af7879b49aa7e2c38a8ba3d773","target":"record","created_at":"2026-07-05T06:28:05Z","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":"70ab541ab55bd83be47730cc650fd8f91bcf22e92f3be725854e9e63d6994c75","cross_cats_sorted":["cs.CV","cs.LG","math.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-07-05T10:00:53Z","title_canon_sha256":"818da87e8b3445bd5f5e160dea3c40aafce1c8af3cb0b8b3e0303eb77ed7e127"},"schema_version":"1.0","source":{"id":"2307.02159","kind":"arxiv","version":1}},"canonical_sha256":"dd396acb4e90993a35ab2c5d12ea21135eab7dd60d65c717ec046a4a3a0e5922","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd396acb4e90993a35ab2c5d12ea21135eab7dd60d65c717ec046a4a3a0e5922","first_computed_at":"2026-07-05T06:28:05.800707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:28:05.800707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QRvFnlEMc0qN/1ATZoSlGYw0X4dK5dCZjbmkUfwva9U6ZhFgNqn+OSUfR2zNk6kqpSUgTCTHdIKqdxipE+YYAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:28:05.801152Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.02159","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c2217cb51096c157eb339db2f2410a7d359a2af7879b49aa7e2c38a8ba3d773","sha256:e3f591bf60a7e976c6bab7cce4fd0447b7fa3dd043e5b31ec354136463f5bb80"],"state_sha256":"5839001866147693f8691177743ebe936af1976430db4e3c53e84a39de3db1f8"}