{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:R5LLWHIXQ672CCH65CMUNWSRJL","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":"79583a40d4366a01f8e2fe90c10c99148315e2144344f08f38fe427a4592b913","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-17T17:59:40Z","title_canon_sha256":"4c9b876bce6a1c28cec14a8d3b70063b1c05496c6063d31d43f0468130baf257"},"schema_version":"1.0","source":{"id":"2307.08702","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.08702","created_at":"2026-07-05T06:31:27Z"},{"alias_kind":"arxiv_version","alias_value":"2307.08702v1","created_at":"2026-07-05T06:31:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08702","created_at":"2026-07-05T06:31:27Z"},{"alias_kind":"pith_short_12","alias_value":"R5LLWHIXQ672","created_at":"2026-07-05T06:31:27Z"},{"alias_kind":"pith_short_16","alias_value":"R5LLWHIXQ672CCH6","created_at":"2026-07-05T06:31:27Z"},{"alias_kind":"pith_short_8","alias_value":"R5LLWHIX","created_at":"2026-07-05T06:31:27Z"}],"graph_snapshots":[{"event_id":"sha256:265c77dedeafdb164adc3af9c826fcca828480c2efe5c13c9e88742792f505d6","target":"graph","created_at":"2026-07-05T06:31:27Z","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/2307.08702/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which uses a single pre-training stage to address both families of tasks simultaneously. We identify diffusion models as a prime candidate. Diffusion models have risen to prominence as a state-of-the-art method for image generation, denoising, inpainting, super-resolution, manipulation, etc. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high fideli","authors_text":"Abhinav Shrivastava, Archana Swaminathan, Matthew Gwilliam, Namitha Padmanabhan, Soumik Mukhopadhyay, Srinidhi Hegde, Tianyi Zhou, Vatsal Agarwal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-17T17:59:40Z","title":"Diffusion Models Beat GANs on Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08702","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":"sha256:6f2493f018a7677b72dab4ce66ca158b18e055ce1b320b0105d23a8583ff6b3b","target":"record","created_at":"2026-07-05T06:31:27Z","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":"79583a40d4366a01f8e2fe90c10c99148315e2144344f08f38fe427a4592b913","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-17T17:59:40Z","title_canon_sha256":"4c9b876bce6a1c28cec14a8d3b70063b1c05496c6063d31d43f0468130baf257"},"schema_version":"1.0","source":{"id":"2307.08702","kind":"arxiv","version":1}},"canonical_sha256":"8f56bb1d1787bfa108fee89946da514af6328506d886dadabd1174f15040cb7d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f56bb1d1787bfa108fee89946da514af6328506d886dadabd1174f15040cb7d","first_computed_at":"2026-07-05T06:31:27.897737Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:31:27.897737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nSq/6h5IdOZBH0rc7lj2Bkh+5UygueIqCHaemDomnDAYK3gDTZyfr3wHyqh8bYs8kvrPd0B1kINAAHOz1YP7AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:31:27.898214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.08702","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f2493f018a7677b72dab4ce66ca158b18e055ce1b320b0105d23a8583ff6b3b","sha256:265c77dedeafdb164adc3af9c826fcca828480c2efe5c13c9e88742792f505d6"],"state_sha256":"1fc5225c6b9134cb50948ae5659e7325ed5a98238066bf9ceef014146970f8aa"}