{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:VHQR5IKTBKYDCQXWXNXFOR3F4A","short_pith_number":"pith:VHQR5IKT","canonical_record":{"source":{"id":"2107.04261","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-09T07:12:39Z","cross_cats_sorted":[],"title_canon_sha256":"4383dae5a4f04792e9822d9ccae3a7335ccaf3fa9243597fda52186e5e1bb03a","abstract_canon_sha256":"e1a1335f2e5fceb26d4a020dba1ec6607948a8cf6ada2ab78c5ba396cb571cb8"},"schema_version":"1.0"},"canonical_sha256":"a9e11ea1530ab03142f6bb6e574765e018d0985bc17028cab5b23673bcde59cd","source":{"kind":"arxiv","id":"2107.04261","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.04261","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"arxiv_version","alias_value":"2107.04261v2","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.04261","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_12","alias_value":"VHQR5IKTBKYD","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_16","alias_value":"VHQR5IKTBKYDCQXW","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_8","alias_value":"VHQR5IKT","created_at":"2026-07-05T04:49:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:VHQR5IKTBKYDCQXWXNXFOR3F4A","target":"record","payload":{"canonical_record":{"source":{"id":"2107.04261","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-09T07:12:39Z","cross_cats_sorted":[],"title_canon_sha256":"4383dae5a4f04792e9822d9ccae3a7335ccaf3fa9243597fda52186e5e1bb03a","abstract_canon_sha256":"e1a1335f2e5fceb26d4a020dba1ec6607948a8cf6ada2ab78c5ba396cb571cb8"},"schema_version":"1.0"},"canonical_sha256":"a9e11ea1530ab03142f6bb6e574765e018d0985bc17028cab5b23673bcde59cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:29.414836Z","signature_b64":"e1oYIFcLZMX8txJp8VcwemsnF8FBa+Kojc77WN8gw/VfFSwyJPQFI2AW+nyRjx16KI+/k7C6YrWqXtVyVPZwDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9e11ea1530ab03142f6bb6e574765e018d0985bc17028cab5b23673bcde59cd","last_reissued_at":"2026-07-05T04:49:29.414396Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:29.414396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.04261","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":"2026-07-05T04:49:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PljZVqJWpm5nOIBWNOLCGLaGHtVhFxLuXZ3mDLyfbhhK7vaseODEZOr4+DPbufQZFoIDy+c+VmjLlqLR3dPYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:13:35.831704Z"},"content_sha256":"ff805ec2a6a643ba76062239b39da57085aedddbbd4cd3d1010d2486f049f8cc","schema_version":"1.0","event_id":"sha256:ff805ec2a6a643ba76062239b39da57085aedddbbd4cd3d1010d2486f049f8cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:VHQR5IKTBKYDCQXWXNXFOR3F4A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Wavelet Transform-assisted Adaptive Generative Modeling for Colorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jin Li, Qiegen Liu, Wanyun Li, Yuhao Wang, Zichen Xu","submitted_at":"2021-07-09T07:12:39Z","abstract_excerpt":"Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the performance is limited owing to the high-dimension of data manifold and model capability. This study presents a novel scheme that exploits the score-based generative model in wavelet domain to address the issues. By taking advantage of the multi-scale and multi-channel representation via wavelet transform, the proposed model learns the richer priors from stacked coarse and detailed wavelet coefficient components jointl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.04261","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/2107.04261/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":"2026-07-05T04:49:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tgG1QbbKODVRnDuc/NBUWWUVM/UKFy9anQnlvQXa2I7lZJvfGIApaUdmiVPVqych8ReyP3m+Aank+o+KeNRNAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:13:35.832721Z"},"content_sha256":"c787c2557577bfa6680075b812ab43dcada2066a78203f9fbce96e978ba8a294","schema_version":"1.0","event_id":"sha256:c787c2557577bfa6680075b812ab43dcada2066a78203f9fbce96e978ba8a294"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/bundle.json","state_url":"https://pith.science/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/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":"2026-08-14T13:13:35Z","links":{"resolver":"https://pith.science/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A","bundle":"https://pith.science/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/bundle.json","state":"https://pith.science/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VHQR5IKTBKYDCQXWXNXFOR3F4A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:VHQR5IKTBKYDCQXWXNXFOR3F4A","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":"e1a1335f2e5fceb26d4a020dba1ec6607948a8cf6ada2ab78c5ba396cb571cb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-09T07:12:39Z","title_canon_sha256":"4383dae5a4f04792e9822d9ccae3a7335ccaf3fa9243597fda52186e5e1bb03a"},"schema_version":"1.0","source":{"id":"2107.04261","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.04261","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"arxiv_version","alias_value":"2107.04261v2","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.04261","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_12","alias_value":"VHQR5IKTBKYD","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_16","alias_value":"VHQR5IKTBKYDCQXW","created_at":"2026-07-05T04:49:29Z"},{"alias_kind":"pith_short_8","alias_value":"VHQR5IKT","created_at":"2026-07-05T04:49:29Z"}],"graph_snapshots":[{"event_id":"sha256:c787c2557577bfa6680075b812ab43dcada2066a78203f9fbce96e978ba8a294","target":"graph","created_at":"2026-07-05T04:49:29Z","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/2107.04261/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the performance is limited owing to the high-dimension of data manifold and model capability. This study presents a novel scheme that exploits the score-based generative model in wavelet domain to address the issues. By taking advantage of the multi-scale and multi-channel representation via wavelet transform, the proposed model learns the richer priors from stacked coarse and detailed wavelet coefficient components jointl","authors_text":"Jin Li, Qiegen Liu, Wanyun Li, Yuhao Wang, Zichen Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-09T07:12:39Z","title":"Wavelet Transform-assisted Adaptive Generative Modeling for Colorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.04261","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:ff805ec2a6a643ba76062239b39da57085aedddbbd4cd3d1010d2486f049f8cc","target":"record","created_at":"2026-07-05T04:49:29Z","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":"e1a1335f2e5fceb26d4a020dba1ec6607948a8cf6ada2ab78c5ba396cb571cb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-09T07:12:39Z","title_canon_sha256":"4383dae5a4f04792e9822d9ccae3a7335ccaf3fa9243597fda52186e5e1bb03a"},"schema_version":"1.0","source":{"id":"2107.04261","kind":"arxiv","version":2}},"canonical_sha256":"a9e11ea1530ab03142f6bb6e574765e018d0985bc17028cab5b23673bcde59cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a9e11ea1530ab03142f6bb6e574765e018d0985bc17028cab5b23673bcde59cd","first_computed_at":"2026-07-05T04:49:29.414396Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:29.414396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e1oYIFcLZMX8txJp8VcwemsnF8FBa+Kojc77WN8gw/VfFSwyJPQFI2AW+nyRjx16KI+/k7C6YrWqXtVyVPZwDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:29.414836Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.04261","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ff805ec2a6a643ba76062239b39da57085aedddbbd4cd3d1010d2486f049f8cc","sha256:c787c2557577bfa6680075b812ab43dcada2066a78203f9fbce96e978ba8a294"],"state_sha256":"e89a84cce3dcc73e57f28d917144c90cbf486173a311b529ef645f333c560e27"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"We70AF6t2C6yvZHKOzifOdroEh4y3mcOrUJwrm6v5w6u5LsJki4y7TGG495y93lm1CM8GELFBo3JtIwVOMRFDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T13:13:35.839580Z","bundle_sha256":"aaea5e27ef4b0029859f0f595dce988298aead5cd7e21994e865bf893940da90"}}