{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3ITFY5OZCAA235BOQ6ZO6AQMM4","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":"c5282cf17108162e2fcee179d63fd76eb71e5a5a65e7054c31dd02554627045f","cross_cats_sorted":["cs.NA","math.NA","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-15T21:54:15Z","title_canon_sha256":"5e8f4ff888ecef5baadd0f3f88aedface8b4025a5e7db55d5b3959a6dbf17a42"},"schema_version":"1.0","source":{"id":"2006.08789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.08789","created_at":"2026-07-05T01:10:43Z"},{"alias_kind":"arxiv_version","alias_value":"2006.08789v1","created_at":"2026-07-05T01:10:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08789","created_at":"2026-07-05T01:10:43Z"},{"alias_kind":"pith_short_12","alias_value":"3ITFY5OZCAA2","created_at":"2026-07-05T01:10:43Z"},{"alias_kind":"pith_short_16","alias_value":"3ITFY5OZCAA235BO","created_at":"2026-07-05T01:10:43Z"},{"alias_kind":"pith_short_8","alias_value":"3ITFY5OZ","created_at":"2026-07-05T01:10:43Z"}],"graph_snapshots":[{"event_id":"sha256:c8be58c8065af59e3a07c3a764acc9b3c19d113069cdd6dc0e1846e626cc7728","target":"graph","created_at":"2026-07-05T01:10:43Z","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/2006.08789/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts to minimizing an energy composed of a data fidelity term and a regularizer. Classically, handcrafted regularizers are used, which are commonly outperformed by state-of-the-art deep learning approaches. In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer. In its core, a convolutional neural network ex","authors_text":"Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock","cross_cats":["cs.NA","math.NA","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-15T21:54:15Z","title":"Total Deep Variation: A Stable Regularizer for Inverse Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08789","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":"sha256:55d41e12f2540340f2ae9dc253cee541b65838bc292d6c972add4a27275b9538","target":"record","created_at":"2026-07-05T01:10:43Z","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":"c5282cf17108162e2fcee179d63fd76eb71e5a5a65e7054c31dd02554627045f","cross_cats_sorted":["cs.NA","math.NA","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-15T21:54:15Z","title_canon_sha256":"5e8f4ff888ecef5baadd0f3f88aedface8b4025a5e7db55d5b3959a6dbf17a42"},"schema_version":"1.0","source":{"id":"2006.08789","kind":"arxiv","version":1}},"canonical_sha256":"da265c75d91001adf42e87b2ef020c671b7672c4280c3bf4e24a7b416493b62d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da265c75d91001adf42e87b2ef020c671b7672c4280c3bf4e24a7b416493b62d","first_computed_at":"2026-07-05T01:10:43.023681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:10:43.023681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3YlybgzWnRpjyN055ujdw6ucEsA8el+Lt0Bwuo0xlQhWni8P/bMaU5lSxHKkDpsN285p8zkR0PHFQoWJ8rYOCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:10:43.024023Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.08789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55d41e12f2540340f2ae9dc253cee541b65838bc292d6c972add4a27275b9538","sha256:c8be58c8065af59e3a07c3a764acc9b3c19d113069cdd6dc0e1846e626cc7728"],"state_sha256":"8a3c1a39a3a5d8633f37bd2efadfd136d1caa957fb4ed3248a159021c92fb29b"}