{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KWLAVYPDZ77WEHCZWXYR7ZVMIB","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":"c300366dd0d792d1aa3dc74b6f09675906c13ed27b743c319863506c0f87b54d","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-11T08:03:18Z","title_canon_sha256":"04f3ae8cf481c997072ec01d6334aa27cfc69f9418ee1edd9775e88b83fe6d6f"},"schema_version":"1.0","source":{"id":"2505.06890","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.06890","created_at":"2026-07-05T11:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"2505.06890v1","created_at":"2026-07-05T11:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06890","created_at":"2026-07-05T11:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"KWLAVYPDZ77W","created_at":"2026-07-05T11:01:38Z"},{"alias_kind":"pith_short_16","alias_value":"KWLAVYPDZ77WEHCZ","created_at":"2026-07-05T11:01:38Z"},{"alias_kind":"pith_short_8","alias_value":"KWLAVYPD","created_at":"2026-07-05T11:01:38Z"}],"graph_snapshots":[{"event_id":"sha256:f89e1708e23eaebb448f0e956a45d60850d588bf16d742b4a36eba6c3dd765b1","target":"graph","created_at":"2026-07-05T11:01:38Z","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/2505.06890/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a diffusion model that integrates a representation-conditioning mechanism, where the representations derived from a Vision Transformer (ViT) are used to condition the internal process of a Transformer-based diffusion model. This approach enables representation-conditioned data generation, addressing the challenge of requiring large-scale labeled datasets by leveraging self-supervised learning on unlabeled data. We evaluate our method through a zero-shot classification task for hematoma detection in brain imaging. Compared to the strong contrastive learning baseline, D","authors_text":"Kosuke Ukita, Tsuyoshi Okita, Ye Xiaolong","cross_cats":["cs.CV","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-11T08:03:18Z","title":"Image Classification Using a Diffusion Model as a Pre-Training Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06890","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":"sha256:7a2526ed8ef58104d0eac8209f2617074aa8e16ca79c4caec7ec9f983172db6a","target":"record","created_at":"2026-07-05T11:01:38Z","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":"c300366dd0d792d1aa3dc74b6f09675906c13ed27b743c319863506c0f87b54d","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-11T08:03:18Z","title_canon_sha256":"04f3ae8cf481c997072ec01d6334aa27cfc69f9418ee1edd9775e88b83fe6d6f"},"schema_version":"1.0","source":{"id":"2505.06890","kind":"arxiv","version":1}},"canonical_sha256":"55960ae1e3cfff621c59b5f11fe6ac40465f3a034ca91965f7bcbc82ad350d1f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55960ae1e3cfff621c59b5f11fe6ac40465f3a034ca91965f7bcbc82ad350d1f","first_computed_at":"2026-07-05T11:01:38.562601Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:01:38.562601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1k4RGpCoQjztEODm5rAP5JKkBGovSzGu4wsjbjQv/2Cmji/dxttfWdlNFXiAdhjiZK8dB11OdXvuutS8y+phBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:01:38.563104Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.06890","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a2526ed8ef58104d0eac8209f2617074aa8e16ca79c4caec7ec9f983172db6a","sha256:f89e1708e23eaebb448f0e956a45d60850d588bf16d742b4a36eba6c3dd765b1"],"state_sha256":"45aef7a8a37fd0cb35aad2cef34807252d894a0e8b0200e5577756b3b3f44111"}