{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:XETWUASNAMCZ3BPAYML52ZWLWF","short_pith_number":"pith:XETWUASN","canonical_record":{"source":{"id":"2012.15020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-30T03:22:46Z","cross_cats_sorted":[],"title_canon_sha256":"93ac7025a512ad4c655831cc05f2b847338c5b4c52b37d404c770ffee2506abe","abstract_canon_sha256":"94cf5c734a26e5a5fca2c434db1b65bbe9443c55875ad15813466db91e846ad9"},"schema_version":"1.0"},"canonical_sha256":"b9276a024d03059d85e0c317dd66cbb14814dc7cc43bb9026bf2a43508407fff","source":{"kind":"arxiv","id":"2012.15020","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.15020","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"arxiv_version","alias_value":"2012.15020v1","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.15020","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_12","alias_value":"XETWUASNAMCZ","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_16","alias_value":"XETWUASNAMCZ3BPA","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_8","alias_value":"XETWUASN","created_at":"2026-07-05T02:04:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:XETWUASNAMCZ3BPAYML52ZWLWF","target":"record","payload":{"canonical_record":{"source":{"id":"2012.15020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-30T03:22:46Z","cross_cats_sorted":[],"title_canon_sha256":"93ac7025a512ad4c655831cc05f2b847338c5b4c52b37d404c770ffee2506abe","abstract_canon_sha256":"94cf5c734a26e5a5fca2c434db1b65bbe9443c55875ad15813466db91e846ad9"},"schema_version":"1.0"},"canonical_sha256":"b9276a024d03059d85e0c317dd66cbb14814dc7cc43bb9026bf2a43508407fff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:50.274802Z","signature_b64":"BOCirDa8EN4FyJwK8Vu8cKGPz1u5qonTFx58gpKGD1Yur/Dye7X332cjt0DJMGMCIJ4KSYg6CnqD7ao6SwB9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9276a024d03059d85e0c317dd66cbb14814dc7cc43bb9026bf2a43508407fff","last_reissued_at":"2026-07-05T02:04:50.274388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:50.274388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.15020","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":"2026-07-05T02:04:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FHlFZgQ7nQz448g00QOquT5GXh45wHmadGSvJyiGC1Q5516iG/padV8PahPAB/1c68iFuTD6GRKBRfEDjbXNCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:56:08.661287Z"},"content_sha256":"89659a19bbf6a5957ae92de3ace8496a70a1277a6d90d584df12cdf8c81f4ef8","schema_version":"1.0","event_id":"sha256:89659a19bbf6a5957ae92de3ace8496a70a1277a6d90d584df12cdf8c81f4ef8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:XETWUASNAMCZ3BPAYML52ZWLWF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lin Ma, Sam Kwong, Shiqi Wang, Wenhan Yang, Zhangkai Ni","submitted_at":"2020-12-30T03:22:46Z","abstract_excerpt":"Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched versions. However, the style and characteristics of photos retouched by experts may not meet the needs or preferences of general users. In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.15020","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/2012.15020/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":"2026-07-05T02:04:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XuupCdDVGJGBRmzs/rqxCHUfCUfGicVyJxTChsFLYvJxJWTXjE98NLF3jpBhZuRFzcZqu/LfC8VpqPLBEf9mBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:56:08.662163Z"},"content_sha256":"9f2dd180ecb1765afa616fb0b55d8db177ef9e1dea7ecb5083ed88271faaa6c3","schema_version":"1.0","event_id":"sha256:9f2dd180ecb1765afa616fb0b55d8db177ef9e1dea7ecb5083ed88271faaa6c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XETWUASNAMCZ3BPAYML52ZWLWF/bundle.json","state_url":"https://pith.science/pith/XETWUASNAMCZ3BPAYML52ZWLWF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XETWUASNAMCZ3BPAYML52ZWLWF/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":"2026-07-05T15:56:08Z","links":{"resolver":"https://pith.science/pith/XETWUASNAMCZ3BPAYML52ZWLWF","bundle":"https://pith.science/pith/XETWUASNAMCZ3BPAYML52ZWLWF/bundle.json","state":"https://pith.science/pith/XETWUASNAMCZ3BPAYML52ZWLWF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XETWUASNAMCZ3BPAYML52ZWLWF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:XETWUASNAMCZ3BPAYML52ZWLWF","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":"94cf5c734a26e5a5fca2c434db1b65bbe9443c55875ad15813466db91e846ad9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-30T03:22:46Z","title_canon_sha256":"93ac7025a512ad4c655831cc05f2b847338c5b4c52b37d404c770ffee2506abe"},"schema_version":"1.0","source":{"id":"2012.15020","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.15020","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"arxiv_version","alias_value":"2012.15020v1","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.15020","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_12","alias_value":"XETWUASNAMCZ","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_16","alias_value":"XETWUASNAMCZ3BPA","created_at":"2026-07-05T02:04:50Z"},{"alias_kind":"pith_short_8","alias_value":"XETWUASN","created_at":"2026-07-05T02:04:50Z"}],"graph_snapshots":[{"event_id":"sha256:9f2dd180ecb1765afa616fb0b55d8db177ef9e1dea7ecb5083ed88271faaa6c3","target":"graph","created_at":"2026-07-05T02:04:50Z","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/2012.15020/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched versions. However, the style and characteristics of photos retouched by experts may not meet the needs or preferences of general users. In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set","authors_text":"Lin Ma, Sam Kwong, Shiqi Wang, Wenhan Yang, Zhangkai Ni","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-30T03:22:46Z","title":"Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.15020","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":"sha256:89659a19bbf6a5957ae92de3ace8496a70a1277a6d90d584df12cdf8c81f4ef8","target":"record","created_at":"2026-07-05T02:04:50Z","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":"94cf5c734a26e5a5fca2c434db1b65bbe9443c55875ad15813466db91e846ad9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-30T03:22:46Z","title_canon_sha256":"93ac7025a512ad4c655831cc05f2b847338c5b4c52b37d404c770ffee2506abe"},"schema_version":"1.0","source":{"id":"2012.15020","kind":"arxiv","version":1}},"canonical_sha256":"b9276a024d03059d85e0c317dd66cbb14814dc7cc43bb9026bf2a43508407fff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9276a024d03059d85e0c317dd66cbb14814dc7cc43bb9026bf2a43508407fff","first_computed_at":"2026-07-05T02:04:50.274388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:04:50.274388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BOCirDa8EN4FyJwK8Vu8cKGPz1u5qonTFx58gpKGD1Yur/Dye7X332cjt0DJMGMCIJ4KSYg6CnqD7ao6SwB9CA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:04:50.274802Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.15020","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89659a19bbf6a5957ae92de3ace8496a70a1277a6d90d584df12cdf8c81f4ef8","sha256:9f2dd180ecb1765afa616fb0b55d8db177ef9e1dea7ecb5083ed88271faaa6c3"],"state_sha256":"fee12e78b08a09a56ca5eb2e2b20da0c78d0980879ebffefa3bfc14309686131"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JwRqzwLA7kvV7b+ozYEjhl1WqucYd86+iF1lBIYurMLoDbDdDDbAUVWTNK/VPHXHFRg38UnpnD/hN9eHKLaPAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T15:56:08.666071Z","bundle_sha256":"b22a862931c84ba2a6d3e15c0aec5386ba64aad57378e32623dd565a4a15f274"}}