{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RXFTOUR4XL2D42DZZBQIWRC52X","short_pith_number":"pith:RXFTOUR4","canonical_record":{"source":{"id":"2303.16411","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T02:41:08Z","cross_cats_sorted":[],"title_canon_sha256":"ade01e5935e301ecec4bad1f8682ee79e884d84923e3700f7a15cdd750436638","abstract_canon_sha256":"76b209e84ae8915e3084802f76bba1d7303e74bd62830624633d6e5ea498a4ab"},"schema_version":"1.0"},"canonical_sha256":"8dcb37523cbaf43e6879c8608b445dd5c34b86a91480727cf1e4e5cc3360ff10","source":{"kind":"arxiv","id":"2303.16411","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.16411","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"arxiv_version","alias_value":"2303.16411v1","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.16411","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_12","alias_value":"RXFTOUR4XL2D","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_16","alias_value":"RXFTOUR4XL2D42DZ","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_8","alias_value":"RXFTOUR4","created_at":"2026-07-05T05:56:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RXFTOUR4XL2D42DZZBQIWRC52X","target":"record","payload":{"canonical_record":{"source":{"id":"2303.16411","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T02:41:08Z","cross_cats_sorted":[],"title_canon_sha256":"ade01e5935e301ecec4bad1f8682ee79e884d84923e3700f7a15cdd750436638","abstract_canon_sha256":"76b209e84ae8915e3084802f76bba1d7303e74bd62830624633d6e5ea498a4ab"},"schema_version":"1.0"},"canonical_sha256":"8dcb37523cbaf43e6879c8608b445dd5c34b86a91480727cf1e4e5cc3360ff10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:06.542664Z","signature_b64":"dm3sjdWFQDIyARlg6D3ZEemKACjkLKTKYmXK1KxEvDfQAtyPRmOYYOMoe/ejPxSNM8bLNOL8cDNJkgZkao2kAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dcb37523cbaf43e6879c8608b445dd5c34b86a91480727cf1e4e5cc3360ff10","last_reissued_at":"2026-07-05T05:56:06.542075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:06.542075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.16411","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":"2026-07-05T05:56:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KIid8f66fI2FsF7Uj2KFckWgVlTzZUpJbBT4BqdgUhjI7RdSVBIXLCk01F8ZED/74S/rUJMxXwl2kxoQto3yDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:50:52.581125Z"},"content_sha256":"50dd0c1e5b531197f36cff7e27121313429badd170469188f1411b839c835434","schema_version":"1.0","event_id":"sha256:50dd0c1e5b531197f36cff7e27121313429badd170469188f1411b839c835434"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RXFTOUR4XL2D42DZZBQIWRC52X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongyi Li, Chunle Guo, Jie Huang, Man Zhou, Naishan Zheng","submitted_at":"2023-03-29T02:41:08Z","abstract_excerpt":"Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while seldom study focuses on loss function. With the question ``are the de facto optimization functions e.g., $L_1$, $L_2$, and perceptual losses optimal?'', we explore the potential of loss and raise our belief ``learned loss function empowers the learning capability of neural networks for image and video restoration''.\n  Concretely, we stand on the sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.16411","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.16411/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":"2026-07-05T05:56:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XvFpH9W4Ydff3hxm/sgB7+Q6FfLXq62qPRjWnY5o1LzvIw7OgfnEvE5yxIrJGiriSLKkEtSZDRZp1XFejWScAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:50:52.581504Z"},"content_sha256":"b6e5a6931df0b54d25e95c8e75dc199f0148eceae6c8c4a94aebae5758940305","schema_version":"1.0","event_id":"sha256:b6e5a6931df0b54d25e95c8e75dc199f0148eceae6c8c4a94aebae5758940305"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RXFTOUR4XL2D42DZZBQIWRC52X/bundle.json","state_url":"https://pith.science/pith/RXFTOUR4XL2D42DZZBQIWRC52X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RXFTOUR4XL2D42DZZBQIWRC52X/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":"2026-07-06T08:50:52Z","links":{"resolver":"https://pith.science/pith/RXFTOUR4XL2D42DZZBQIWRC52X","bundle":"https://pith.science/pith/RXFTOUR4XL2D42DZZBQIWRC52X/bundle.json","state":"https://pith.science/pith/RXFTOUR4XL2D42DZZBQIWRC52X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RXFTOUR4XL2D42DZZBQIWRC52X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RXFTOUR4XL2D42DZZBQIWRC52X","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":"76b209e84ae8915e3084802f76bba1d7303e74bd62830624633d6e5ea498a4ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T02:41:08Z","title_canon_sha256":"ade01e5935e301ecec4bad1f8682ee79e884d84923e3700f7a15cdd750436638"},"schema_version":"1.0","source":{"id":"2303.16411","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.16411","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"arxiv_version","alias_value":"2303.16411v1","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.16411","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_12","alias_value":"RXFTOUR4XL2D","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_16","alias_value":"RXFTOUR4XL2D42DZ","created_at":"2026-07-05T05:56:06Z"},{"alias_kind":"pith_short_8","alias_value":"RXFTOUR4","created_at":"2026-07-05T05:56:06Z"}],"graph_snapshots":[{"event_id":"sha256:b6e5a6931df0b54d25e95c8e75dc199f0148eceae6c8c4a94aebae5758940305","target":"graph","created_at":"2026-07-05T05:56:06Z","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/2303.16411/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while seldom study focuses on loss function. With the question ``are the de facto optimization functions e.g., $L_1$, $L_2$, and perceptual losses optimal?'', we explore the potential of loss and raise our belief ``learned loss function empowers the learning capability of neural networks for image and video restoration''.\n  Concretely, we stand on the sho","authors_text":"Chongyi Li, Chunle Guo, Jie Huang, Man Zhou, Naishan Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T02:41:08Z","title":"Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.16411","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":"sha256:50dd0c1e5b531197f36cff7e27121313429badd170469188f1411b839c835434","target":"record","created_at":"2026-07-05T05:56:06Z","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":"76b209e84ae8915e3084802f76bba1d7303e74bd62830624633d6e5ea498a4ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T02:41:08Z","title_canon_sha256":"ade01e5935e301ecec4bad1f8682ee79e884d84923e3700f7a15cdd750436638"},"schema_version":"1.0","source":{"id":"2303.16411","kind":"arxiv","version":1}},"canonical_sha256":"8dcb37523cbaf43e6879c8608b445dd5c34b86a91480727cf1e4e5cc3360ff10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8dcb37523cbaf43e6879c8608b445dd5c34b86a91480727cf1e4e5cc3360ff10","first_computed_at":"2026-07-05T05:56:06.542075Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:56:06.542075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dm3sjdWFQDIyARlg6D3ZEemKACjkLKTKYmXK1KxEvDfQAtyPRmOYYOMoe/ejPxSNM8bLNOL8cDNJkgZkao2kAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:56:06.542664Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.16411","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:50dd0c1e5b531197f36cff7e27121313429badd170469188f1411b839c835434","sha256:b6e5a6931df0b54d25e95c8e75dc199f0148eceae6c8c4a94aebae5758940305"],"state_sha256":"fae70ba9ec43ca11ec33e1575e3d2f12ab126fccadf23fb591026280b863ca21"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CaVYivYEsY9U7p1rDXduCSGmBJfIxTE/3CDpZ0092K1DjZfQE7T8cd8e5oitSpEZ/uMlEdSI6tds6RZqnw5TBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:50:52.583346Z","bundle_sha256":"9dbafc9d58252b9a757de05284c6fa1720fa9e9cdd4a343e0e4a429e3c27004d"}}