{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TUNMXAQ62UC24FWD6ZVOW5MIFG","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":"14bad1bf802f5955cfd26e4301b3d0814e6ebc3f3d49e9604c0ecfe06d96e10e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-02T07:58:26Z","title_canon_sha256":"ef5464af6bf7d6992dbcbc18bdd0d46332b785215a6812c61ac6b7557c3e99d0"},"schema_version":"1.0","source":{"id":"2501.01127","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.01127","created_at":"2026-07-05T09:56:15Z"},{"alias_kind":"arxiv_version","alias_value":"2501.01127v1","created_at":"2026-07-05T09:56:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01127","created_at":"2026-07-05T09:56:15Z"},{"alias_kind":"pith_short_12","alias_value":"TUNMXAQ62UC2","created_at":"2026-07-05T09:56:15Z"},{"alias_kind":"pith_short_16","alias_value":"TUNMXAQ62UC24FWD","created_at":"2026-07-05T09:56:15Z"},{"alias_kind":"pith_short_8","alias_value":"TUNMXAQ6","created_at":"2026-07-05T09:56:15Z"}],"graph_snapshots":[{"event_id":"sha256:38e9b6a8402ffe7f188e0f5668fb302ca8d502965c681a808f3d1e0cf839ce7f","target":"graph","created_at":"2026-07-05T09:56:15Z","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/2501.01127/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to the analysis. Deep learning can be powerful for such tasks, but surprisingly their combination with a focus on interpretability and generalizability is rarely explored. In this work, we introduce a novel framework for interpretable deep image decomposition, combining hierarchical Bayesian modeling and deep learning to create an architecture-modularized and model-generalizable deep neural network (DNN). The proposed fra","authors_text":"Fuping Wu, Shangqi Gao, Sihan Wang, Xiahai Zhuang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-02T07:58:26Z","title":"InDeed: Interpretable image deep decomposition with guaranteed generalizability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01127","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:ee145530384dc74b67dfe453265b8b95204c5435161ab89ecc6c0978809211e6","target":"record","created_at":"2026-07-05T09:56:15Z","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":"14bad1bf802f5955cfd26e4301b3d0814e6ebc3f3d49e9604c0ecfe06d96e10e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-02T07:58:26Z","title_canon_sha256":"ef5464af6bf7d6992dbcbc18bdd0d46332b785215a6812c61ac6b7557c3e99d0"},"schema_version":"1.0","source":{"id":"2501.01127","kind":"arxiv","version":1}},"canonical_sha256":"9d1acb821ed505ae16c3f66aeb758829b07482ffe1d6ffbe072bde562d22e288","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d1acb821ed505ae16c3f66aeb758829b07482ffe1d6ffbe072bde562d22e288","first_computed_at":"2026-07-05T09:56:15.235870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:15.235870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kon1RY0/zwwAFOCyiQ73Fn8CqHIuuku5Af6rWalpeE54B9soJmiXJF/jwhR5yluxWB60iHpaR0zVQeFrarWqDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:15.236362Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.01127","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee145530384dc74b67dfe453265b8b95204c5435161ab89ecc6c0978809211e6","sha256:38e9b6a8402ffe7f188e0f5668fb302ca8d502965c681a808f3d1e0cf839ce7f"],"state_sha256":"4f90e081582e83676620da0098e229798829e3c5077a38cbc97a10a03958ed83"}