{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:L3VAI5LTZ4QLIDCUS5ID5EEGD6","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":"d0b85079e3fca4f34b343f234cefcd8ee690ae0473a762ce18259a0ffbf129c7","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-31T16:24:34Z","title_canon_sha256":"113170998ef321a599262f2f484a6d11add408319072c6f317638386963ae8c9"},"schema_version":"1.0","source":{"id":"2301.13743","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13743","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13743v1","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13743","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"L3VAI5LTZ4QL","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"L3VAI5LTZ4QLIDCU","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"L3VAI5LT","created_at":"2026-07-05T05:37:18Z"}],"graph_snapshots":[{"event_id":"sha256:a7a30baa4387e283f102f7d29a8eef23ed2e0dbee5953b3384cfb727c38c9afe","target":"graph","created_at":"2026-07-05T05:37:18Z","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/2301.13743/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cross-modality data translation has attracted great interest in image computing. Deep generative models (\\textit{e.g.}, GANs) show performance improvement in tackling those problems. Nevertheless, as a fundamental challenge in image translation, the problem of Zero-shot-Learning Cross-Modality Data Translation with fidelity remains unanswered. This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain. The MIDiffusion leverag","authors_text":"Herv\\'e Delingette, Maxime Sermesant, Ona Wu, Yingyu Yang, Zihao Wang","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-31T16:24:34Z","title":"Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13743","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":"sha256:3f9f1419f2c2ddd50ec6ee8f10daa66a0bf0b6dd8a3007324133a57ac1ce7009","target":"record","created_at":"2026-07-05T05:37:18Z","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":"d0b85079e3fca4f34b343f234cefcd8ee690ae0473a762ce18259a0ffbf129c7","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-31T16:24:34Z","title_canon_sha256":"113170998ef321a599262f2f484a6d11add408319072c6f317638386963ae8c9"},"schema_version":"1.0","source":{"id":"2301.13743","kind":"arxiv","version":1}},"canonical_sha256":"5eea047573cf20b40c5497503e90861fb0f75de5b880f664ab34a2b0525379fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5eea047573cf20b40c5497503e90861fb0f75de5b880f664ab34a2b0525379fc","first_computed_at":"2026-07-05T05:37:18.134580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:18.134580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ml49iLCLVicy/XdU3cXlUGD/Hl6qkTPHIM+HW76ufAKTjsP2Wmxog7MXDdy8gIwR5eLRdh/xLQhdLKJn0ANhBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:18.135064Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13743","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f9f1419f2c2ddd50ec6ee8f10daa66a0bf0b6dd8a3007324133a57ac1ce7009","sha256:a7a30baa4387e283f102f7d29a8eef23ed2e0dbee5953b3384cfb727c38c9afe"],"state_sha256":"54b14900f2c823eec47ac80741747a16d5014c82e14f9f311925dc72ffb70ccb"}