{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:S2LKD7AXAQPZFPBBY7RWCMIR2C","short_pith_number":"pith:S2LKD7AX","canonical_record":{"source":{"id":"2204.00227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-01T06:22:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"45c713b6953552e7ffcd017114af23cba44f81f569504c4d977452a2f20f56bb","abstract_canon_sha256":"8a422f52bc9da8a17b2d70a346e553d19e03bb2d22023a01e03cb11357fc836f"},"schema_version":"1.0"},"canonical_sha256":"9696a1fc17041f92bc21c7e3613111d08832276eebbce541680fcae8ba14bbf6","source":{"kind":"arxiv","id":"2204.00227","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.00227","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"2204.00227v1","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.00227","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"S2LKD7AXAQPZ","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_16","alias_value":"S2LKD7AXAQPZFPBB","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_8","alias_value":"S2LKD7AX","created_at":"2026-07-05T04:10:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:S2LKD7AXAQPZFPBBY7RWCMIR2C","target":"record","payload":{"canonical_record":{"source":{"id":"2204.00227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-01T06:22:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"45c713b6953552e7ffcd017114af23cba44f81f569504c4d977452a2f20f56bb","abstract_canon_sha256":"8a422f52bc9da8a17b2d70a346e553d19e03bb2d22023a01e03cb11357fc836f"},"schema_version":"1.0"},"canonical_sha256":"9696a1fc17041f92bc21c7e3613111d08832276eebbce541680fcae8ba14bbf6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:44.662486Z","signature_b64":"8JBBRIhu9XOhN1rSGyv6Kjlv9WBJWKpmhpLeTz4QPqzzVypQvD4/W5VQWAPQR5n7h+b6qxivouU6MsmwpShADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9696a1fc17041f92bc21c7e3613111d08832276eebbce541680fcae8ba14bbf6","last_reissued_at":"2026-07-05T04:10:44.662004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:44.662004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.00227","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":"2026-07-05T04:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MRb3it92uT3QKLrm6z5aouBXaJzgl5b4wn7hwXcA1w10EOxIvJLwXskk0rb8aOYVEbHFdc6uLyjEDZJqWc36CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T17:50:32.520365Z"},"content_sha256":"080b376d85a80f1932a35e93f088a8a2186c6431bc9c6955d8181109bc85c0d8","schema_version":"1.0","event_id":"sha256:080b376d85a80f1932a35e93f088a8a2186c6431bc9c6955d8181109bc85c0d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:S2LKD7AXAQPZFPBBY7RWCMIR2C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Perception Prioritized Training of Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chaehun Shin, Hyunwoo Kim, Jooyoung Choi, Jungbeom Lee, Sungroh Yoon, Sungwon Kim","submitted_at":"2022-04-01T06:22:23Z","abstract_excerpt":"Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function. We show that our simple redesign of the weighting scheme significantly improves the performance of diffusion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.00227","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/2204.00227/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":"2026-07-05T04:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tXST2KxMQ/b1Rmc00dX+7C7uSDOnTIdMvM11kH2sKAzJ9POrPlVczeYCymEA06VOzVMTkOFztVsrYsYSsb6bAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T17:50:32.520754Z"},"content_sha256":"e3c3eca90046acb824c15b7e3f1e1aca81b4e1f7591b8b38ddf7c99c5bcf9eea","schema_version":"1.0","event_id":"sha256:e3c3eca90046acb824c15b7e3f1e1aca81b4e1f7591b8b38ddf7c99c5bcf9eea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/bundle.json","state_url":"https://pith.science/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/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":"2026-07-11T17:50:32Z","links":{"resolver":"https://pith.science/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C","bundle":"https://pith.science/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/bundle.json","state":"https://pith.science/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S2LKD7AXAQPZFPBBY7RWCMIR2C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:S2LKD7AXAQPZFPBBY7RWCMIR2C","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":"8a422f52bc9da8a17b2d70a346e553d19e03bb2d22023a01e03cb11357fc836f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-01T06:22:23Z","title_canon_sha256":"45c713b6953552e7ffcd017114af23cba44f81f569504c4d977452a2f20f56bb"},"schema_version":"1.0","source":{"id":"2204.00227","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.00227","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"2204.00227v1","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.00227","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"S2LKD7AXAQPZ","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_16","alias_value":"S2LKD7AXAQPZFPBB","created_at":"2026-07-05T04:10:44Z"},{"alias_kind":"pith_short_8","alias_value":"S2LKD7AX","created_at":"2026-07-05T04:10:44Z"}],"graph_snapshots":[{"event_id":"sha256:e3c3eca90046acb824c15b7e3f1e1aca81b4e1f7591b8b38ddf7c99c5bcf9eea","target":"graph","created_at":"2026-07-05T04:10:44Z","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/2204.00227/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function. We show that our simple redesign of the weighting scheme significantly improves the performance of diffusion","authors_text":"Chaehun Shin, Hyunwoo Kim, Jooyoung Choi, Jungbeom Lee, Sungroh Yoon, Sungwon Kim","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-01T06:22:23Z","title":"Perception Prioritized Training of Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.00227","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":"sha256:080b376d85a80f1932a35e93f088a8a2186c6431bc9c6955d8181109bc85c0d8","target":"record","created_at":"2026-07-05T04:10:44Z","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":"8a422f52bc9da8a17b2d70a346e553d19e03bb2d22023a01e03cb11357fc836f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-01T06:22:23Z","title_canon_sha256":"45c713b6953552e7ffcd017114af23cba44f81f569504c4d977452a2f20f56bb"},"schema_version":"1.0","source":{"id":"2204.00227","kind":"arxiv","version":1}},"canonical_sha256":"9696a1fc17041f92bc21c7e3613111d08832276eebbce541680fcae8ba14bbf6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9696a1fc17041f92bc21c7e3613111d08832276eebbce541680fcae8ba14bbf6","first_computed_at":"2026-07-05T04:10:44.662004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:44.662004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8JBBRIhu9XOhN1rSGyv6Kjlv9WBJWKpmhpLeTz4QPqzzVypQvD4/W5VQWAPQR5n7h+b6qxivouU6MsmwpShADQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:44.662486Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.00227","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:080b376d85a80f1932a35e93f088a8a2186c6431bc9c6955d8181109bc85c0d8","sha256:e3c3eca90046acb824c15b7e3f1e1aca81b4e1f7591b8b38ddf7c99c5bcf9eea"],"state_sha256":"04891a3b7779433a0f33a50518fc82af9561e8039cd4764263118a9130205c9a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4CkjMRTEgDfu+nBov9/xp6yvP29nEgXK1Asla6RiqCcI3qLYmUm8PTj8lOivApN2O0SYKLTuqyNk5mpjSSPnAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-11T17:50:32.522938Z","bundle_sha256":"fffa129734ae12b781e798b2bd7811c0d49db147f3c4ca63512a8f1a186e418b"}}