{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:2DCD6BEVS2ELHMB6IRZR4GPXU4","short_pith_number":"pith:2DCD6BEV","canonical_record":{"source":{"id":"1703.09964","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-29T10:51:49Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"0da2184c9896ddcd55ccc73459719db9dfb8167d03430332edc001a5d2ca8b4d","abstract_canon_sha256":"1dcfb79b1c99a7f90303337994dbac41ff1ec195ce8a036de90b2e1e9e06d241"},"schema_version":"1.0"},"canonical_sha256":"d0c43f04959688b3b03e44731e19f7a71115743fa5d18dc6bd1f0c9a73123977","source":{"kind":"arxiv","id":"1703.09964","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09964","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09964v1","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09964","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"pith_short_12","alias_value":"2DCD6BEVS2EL","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2DCD6BEVS2ELHMB6","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2DCD6BEV","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:2DCD6BEVS2ELHMB6IRZR4GPXU4","target":"record","payload":{"canonical_record":{"source":{"id":"1703.09964","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-29T10:51:49Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"0da2184c9896ddcd55ccc73459719db9dfb8167d03430332edc001a5d2ca8b4d","abstract_canon_sha256":"1dcfb79b1c99a7f90303337994dbac41ff1ec195ce8a036de90b2e1e9e06d241"},"schema_version":"1.0"},"canonical_sha256":"d0c43f04959688b3b03e44731e19f7a71115743fa5d18dc6bd1f0c9a73123977","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:47:38.923059Z","signature_b64":"EjrG5cOETfEBeFO2ygNEum3mod945NeUEaqqwFT4I8gxo3fcMcVgfqKiqPk0ranyDMJ1wsv+FmP77js9Bjj6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0c43f04959688b3b03e44731e19f7a71115743fa5d18dc6bd1f0c9a73123977","last_reissued_at":"2026-05-18T00:47:38.922490Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:47:38.922490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.09964","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:47:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DU9DG2QRxDjqNhV4eBETBEDDX6IbpuGNRL4rMGFvdDAxtUAY4uhfeUJzwB6b2ZBHti1STwuxtYoGReYUnbHPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:57:38.207676Z"},"content_sha256":"41862f6e6d48d679b8c3535080dc3cea33a8d89faea8471e7706a7c869df03ef","schema_version":"1.0","event_id":"sha256:41862f6e6d48d679b8c3535080dc3cea33a8d89faea8471e7706a7c869df03ef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:2DCD6BEVS2ELHMB6IRZR4GPXU4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image Restoration using Autoencoding Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Matthias Zwicker, Siavash Arjomand Bigdeli","submitted_at":"2017-03-29T10:51:49Z","abstract_excerpt":"We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the auto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09964","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:47:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GygO/UhQJxSGEdOjMnPk2CetsJnaDEktjppszZ7Aopg1fC53qJIJoNBrQ/Cm/jGrAGH1543fY+E3LpHSstquCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:57:38.208019Z"},"content_sha256":"4dd2535e15604239a1b68682a7ae8e5000a960da43d548a8fedf1bb8b8ccc156","schema_version":"1.0","event_id":"sha256:4dd2535e15604239a1b68682a7ae8e5000a960da43d548a8fedf1bb8b8ccc156"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/bundle.json","state_url":"https://pith.science/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T22:57:38Z","links":{"resolver":"https://pith.science/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4","bundle":"https://pith.science/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/bundle.json","state":"https://pith.science/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2DCD6BEVS2ELHMB6IRZR4GPXU4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:2DCD6BEVS2ELHMB6IRZR4GPXU4","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":"1dcfb79b1c99a7f90303337994dbac41ff1ec195ce8a036de90b2e1e9e06d241","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-29T10:51:49Z","title_canon_sha256":"0da2184c9896ddcd55ccc73459719db9dfb8167d03430332edc001a5d2ca8b4d"},"schema_version":"1.0","source":{"id":"1703.09964","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09964","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09964v1","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09964","created_at":"2026-05-18T00:47:38Z"},{"alias_kind":"pith_short_12","alias_value":"2DCD6BEVS2EL","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2DCD6BEVS2ELHMB6","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2DCD6BEV","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:4dd2535e15604239a1b68682a7ae8e5000a960da43d548a8fedf1bb8b8ccc156","target":"graph","created_at":"2026-05-18T00:47:38Z","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"},"paper":{"abstract_excerpt":"We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the auto","authors_text":"Matthias Zwicker, Siavash Arjomand Bigdeli","cross_cats":["cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-29T10:51:49Z","title":"Image Restoration using Autoencoding Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09964","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":"sha256:41862f6e6d48d679b8c3535080dc3cea33a8d89faea8471e7706a7c869df03ef","target":"record","created_at":"2026-05-18T00:47:38Z","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":"1dcfb79b1c99a7f90303337994dbac41ff1ec195ce8a036de90b2e1e9e06d241","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-29T10:51:49Z","title_canon_sha256":"0da2184c9896ddcd55ccc73459719db9dfb8167d03430332edc001a5d2ca8b4d"},"schema_version":"1.0","source":{"id":"1703.09964","kind":"arxiv","version":1}},"canonical_sha256":"d0c43f04959688b3b03e44731e19f7a71115743fa5d18dc6bd1f0c9a73123977","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0c43f04959688b3b03e44731e19f7a71115743fa5d18dc6bd1f0c9a73123977","first_computed_at":"2026-05-18T00:47:38.922490Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:47:38.922490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EjrG5cOETfEBeFO2ygNEum3mod945NeUEaqqwFT4I8gxo3fcMcVgfqKiqPk0ranyDMJ1wsv+FmP77js9Bjj6AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:47:38.923059Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.09964","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41862f6e6d48d679b8c3535080dc3cea33a8d89faea8471e7706a7c869df03ef","sha256:4dd2535e15604239a1b68682a7ae8e5000a960da43d548a8fedf1bb8b8ccc156"],"state_sha256":"c53f5f90764a5b6c830ad1d74d0be4b754f34b5735f5baafecda6176a9f3d1c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XwpsQ6GI4pVIWLqUXZ++kywqtQ95vk3WInL5y/lTJgUSe9DB1pfn9Msu1tUn9hdf6FW+cqzydy4x3blilBSpBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T22:57:38.209972Z","bundle_sha256":"5e5e11410ea16bcb95f66d95b461d383bef1e18dd11845d24345df16a60f5b1c"}}