{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YJN4KGYQLNC26BKNQWBRJGFIGG","short_pith_number":"pith:YJN4KGYQ","canonical_record":{"source":{"id":"2305.05651","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-09T17:49:27Z","cross_cats_sorted":[],"title_canon_sha256":"e665241b923df0070f606826f71ee05379c0db6a599789a7b5f1c38954efb4ee","abstract_canon_sha256":"384e5e3e52d9d2b9137a89c4a7d21718416356244a85fa0de4f04a4d8703b424"},"schema_version":"1.0"},"canonical_sha256":"c25bc51b105b45af054d85831498a831bf216c8df1f4e6c00d66240b97d0ea7f","source":{"kind":"arxiv","id":"2305.05651","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.05651","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"arxiv_version","alias_value":"2305.05651v2","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05651","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_12","alias_value":"YJN4KGYQLNC2","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_16","alias_value":"YJN4KGYQLNC26BKN","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_8","alias_value":"YJN4KGYQ","created_at":"2026-07-05T09:54:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YJN4KGYQLNC26BKNQWBRJGFIGG","target":"record","payload":{"canonical_record":{"source":{"id":"2305.05651","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-09T17:49:27Z","cross_cats_sorted":[],"title_canon_sha256":"e665241b923df0070f606826f71ee05379c0db6a599789a7b5f1c38954efb4ee","abstract_canon_sha256":"384e5e3e52d9d2b9137a89c4a7d21718416356244a85fa0de4f04a4d8703b424"},"schema_version":"1.0"},"canonical_sha256":"c25bc51b105b45af054d85831498a831bf216c8df1f4e6c00d66240b97d0ea7f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:42.648636Z","signature_b64":"wPv+I3y3MPsgLR2BGQkpMZJZay6AfJnZAQM6CYhS6xYDVTpF9LImDviLDLt9yqzDspKT6Ta4JIZdJrg1OrcSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c25bc51b105b45af054d85831498a831bf216c8df1f4e6c00d66240b97d0ea7f","last_reissued_at":"2026-07-05T09:54:42.648069Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:42.648069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.05651","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:54:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ONcQ0Dw/NRoItwPX062fqoWc/OYvj209M4uPyn7yYcIOGAQ2psAFQsHqNzraQ1x5FUUzqsWkSPtoa4B6k7dHBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:18:23.560609Z"},"content_sha256":"57c362ea78b2edb2e8c5e7dffddcd634349d43739058cc01eac769dea7353c98","schema_version":"1.0","event_id":"sha256:57c362ea78b2edb2e8c5e7dffddcd634349d43739058cc01eac769dea7353c98"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YJN4KGYQLNC26BKNQWBRJGFIGG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Leopold Parts, Mikhail Papkov, Pavel Chizhov","submitted_at":"2023-05-09T17:49:27Z","abstract_excerpt":"Self-supervised image denoising implies restoring the signal from a noisy image without access to the ground truth. State-of-the-art solutions for this task rely on predicting masked pixels with a fully-convolutional neural network. This most often requires multiple forward passes, information about the noise model, or intricate regularization functions. In this paper, we propose a Swin Transformer-based Image Autoencoder (SwinIA), the first fully-transformer architecture for self-supervised denoising. The flexibility of the attention mechanism helps to fulfill the blind-spot property that con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05651","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.05651/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T09:54:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jd4zYWhX+7NF+9sR7tD5yrHki06oufVtd0YgY05Glt1S6vJDKo2DsRJpcP/hwT6Vv4ef15KQGLkPHcIEZ/ypCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:18:23.561547Z"},"content_sha256":"ed0303ce957e827e8eb14d17519752557ad0b11cf0661a4317a94870d0f1132d","schema_version":"1.0","event_id":"sha256:ed0303ce957e827e8eb14d17519752557ad0b11cf0661a4317a94870d0f1132d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/bundle.json","state_url":"https://pith.science/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/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":"2026-08-17T18:18:23Z","links":{"resolver":"https://pith.science/pith/YJN4KGYQLNC26BKNQWBRJGFIGG","bundle":"https://pith.science/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/bundle.json","state":"https://pith.science/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YJN4KGYQLNC26BKNQWBRJGFIGG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YJN4KGYQLNC26BKNQWBRJGFIGG","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":"384e5e3e52d9d2b9137a89c4a7d21718416356244a85fa0de4f04a4d8703b424","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-09T17:49:27Z","title_canon_sha256":"e665241b923df0070f606826f71ee05379c0db6a599789a7b5f1c38954efb4ee"},"schema_version":"1.0","source":{"id":"2305.05651","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.05651","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"arxiv_version","alias_value":"2305.05651v2","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05651","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_12","alias_value":"YJN4KGYQLNC2","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_16","alias_value":"YJN4KGYQLNC26BKN","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_8","alias_value":"YJN4KGYQ","created_at":"2026-07-05T09:54:42Z"}],"graph_snapshots":[{"event_id":"sha256:ed0303ce957e827e8eb14d17519752557ad0b11cf0661a4317a94870d0f1132d","target":"graph","created_at":"2026-07-05T09:54:42Z","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/2305.05651/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised image denoising implies restoring the signal from a noisy image without access to the ground truth. State-of-the-art solutions for this task rely on predicting masked pixels with a fully-convolutional neural network. This most often requires multiple forward passes, information about the noise model, or intricate regularization functions. In this paper, we propose a Swin Transformer-based Image Autoencoder (SwinIA), the first fully-transformer architecture for self-supervised denoising. The flexibility of the attention mechanism helps to fulfill the blind-spot property that con","authors_text":"Leopold Parts, Mikhail Papkov, Pavel Chizhov","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-09T17:49:27Z","title":"SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05651","kind":"arxiv","version":2},"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":"sha256:57c362ea78b2edb2e8c5e7dffddcd634349d43739058cc01eac769dea7353c98","target":"record","created_at":"2026-07-05T09:54:42Z","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":"384e5e3e52d9d2b9137a89c4a7d21718416356244a85fa0de4f04a4d8703b424","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-09T17:49:27Z","title_canon_sha256":"e665241b923df0070f606826f71ee05379c0db6a599789a7b5f1c38954efb4ee"},"schema_version":"1.0","source":{"id":"2305.05651","kind":"arxiv","version":2}},"canonical_sha256":"c25bc51b105b45af054d85831498a831bf216c8df1f4e6c00d66240b97d0ea7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c25bc51b105b45af054d85831498a831bf216c8df1f4e6c00d66240b97d0ea7f","first_computed_at":"2026-07-05T09:54:42.648069Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:42.648069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wPv+I3y3MPsgLR2BGQkpMZJZay6AfJnZAQM6CYhS6xYDVTpF9LImDviLDLt9yqzDspKT6Ta4JIZdJrg1OrcSCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:42.648636Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.05651","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57c362ea78b2edb2e8c5e7dffddcd634349d43739058cc01eac769dea7353c98","sha256:ed0303ce957e827e8eb14d17519752557ad0b11cf0661a4317a94870d0f1132d"],"state_sha256":"d8463564d108e7c141fa47eb9608aa4d9d2ed1f962b532468634c5c67cba7fef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xD7OpFwv0N5IPu8H8CiX4D5SaUuKhxwNQOLbQPEIVgHehM0h8y9LR4KfBnMYVoU4oOMoHEE+ETsk9TVf43gtAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T18:18:23.569114Z","bundle_sha256":"74d8aa04acd733e7b37070bf7a51da822bb1f69c39207de231aef604dbea462a"}}