{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4GD3T7YV2DYO6XLF7YQA4S5PWQ","short_pith_number":"pith:4GD3T7YV","canonical_record":{"source":{"id":"1903.05820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T05:33:08Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"838e6c9f4760c99665925e6a6e1ac7ebf4042f518d33b5b2ed38dfc0f7912a6c","abstract_canon_sha256":"fddd03e206a9aaf644f81c65a75bac0e6ac3e771436d118a77841eee56f9b6b1"},"schema_version":"1.0"},"canonical_sha256":"e187b9ff15d0f0ef5d65fe200e4bafb432afa27db1a27bc1a44cb7dd78891ffc","source":{"kind":"arxiv","id":"1903.05820","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05820","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05820v1","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05820","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"pith_short_12","alias_value":"4GD3T7YV2DYO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4GD3T7YV2DYO6XLF","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4GD3T7YV","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4GD3T7YV2DYO6XLF7YQA4S5PWQ","target":"record","payload":{"canonical_record":{"source":{"id":"1903.05820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T05:33:08Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"838e6c9f4760c99665925e6a6e1ac7ebf4042f518d33b5b2ed38dfc0f7912a6c","abstract_canon_sha256":"fddd03e206a9aaf644f81c65a75bac0e6ac3e771436d118a77841eee56f9b6b1"},"schema_version":"1.0"},"canonical_sha256":"e187b9ff15d0f0ef5d65fe200e4bafb432afa27db1a27bc1a44cb7dd78891ffc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:16.084406Z","signature_b64":"8fQtKHEcPSy4n/FUQt87S3EBPoaW+9U8H9EVM2U5s6JkSDHCBzHrD/byWgo9O1l+7my/PqjZr0xwgtTG49MdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e187b9ff15d0f0ef5d65fe200e4bafb432afa27db1a27bc1a44cb7dd78891ffc","last_reissued_at":"2026-05-17T23:51:16.083908Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:16.083908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.05820","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":"2026-05-17T23:51:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gqVBpl9M1Hx64S3phUNUoD8hYzlG/tPfolaLpx9+CGhQ1VcZ9CynaYPgkpw33jUhvDK0IE8afYluJ/o6lt+iAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T08:07:35.662404Z"},"content_sha256":"0c184a7de00dfcb40f4e31927cf83a36fb7ca5a17e05007cf209aedf273cf0f4","schema_version":"1.0","event_id":"sha256:0c184a7de00dfcb40f4e31927cf83a36fb7ca5a17e05007cf209aedf273cf0f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4GD3T7YV2DYO6XLF7YQA4S5PWQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Huibing Wang, Ibrahim Shehi Shehu, Tongtong Zhao, Xianping Fu, Yuxiao Yan","submitted_at":"2019-03-14T05:33:08Z","abstract_excerpt":"Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05820","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":""},"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":"2026-05-17T23:51:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4GrLI9z4i7fa1J6mg+vqSRxMEiMZbuzo78VFz/vGFmPETX40w1ENXoY8afQjD7rHEpXJmpmZY2q/uX9umdsZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T08:07:35.662854Z"},"content_sha256":"d917f22e54f1ee897f22dcf2603fc7244c205b8024fdb6d625bd22e65ef42e54","schema_version":"1.0","event_id":"sha256:d917f22e54f1ee897f22dcf2603fc7244c205b8024fdb6d625bd22e65ef42e54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/bundle.json","state_url":"https://pith.science/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/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":"2026-05-25T08:07:35Z","links":{"resolver":"https://pith.science/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ","bundle":"https://pith.science/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/bundle.json","state":"https://pith.science/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GD3T7YV2DYO6XLF7YQA4S5PWQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4GD3T7YV2DYO6XLF7YQA4S5PWQ","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":"fddd03e206a9aaf644f81c65a75bac0e6ac3e771436d118a77841eee56f9b6b1","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T05:33:08Z","title_canon_sha256":"838e6c9f4760c99665925e6a6e1ac7ebf4042f518d33b5b2ed38dfc0f7912a6c"},"schema_version":"1.0","source":{"id":"1903.05820","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05820","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05820v1","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05820","created_at":"2026-05-17T23:51:16Z"},{"alias_kind":"pith_short_12","alias_value":"4GD3T7YV2DYO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4GD3T7YV2DYO6XLF","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4GD3T7YV","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:d917f22e54f1ee897f22dcf2603fc7244c205b8024fdb6d625bd22e65ef42e54","target":"graph","created_at":"2026-05-17T23:51:16Z","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"},"paper":{"abstract_excerpt":"Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the","authors_text":"Huibing Wang, Ibrahim Shehi Shehu, Tongtong Zhao, Xianping Fu, Yuxiao Yan","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T05:33:08Z","title":"Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05820","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":"sha256:0c184a7de00dfcb40f4e31927cf83a36fb7ca5a17e05007cf209aedf273cf0f4","target":"record","created_at":"2026-05-17T23:51:16Z","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":"fddd03e206a9aaf644f81c65a75bac0e6ac3e771436d118a77841eee56f9b6b1","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T05:33:08Z","title_canon_sha256":"838e6c9f4760c99665925e6a6e1ac7ebf4042f518d33b5b2ed38dfc0f7912a6c"},"schema_version":"1.0","source":{"id":"1903.05820","kind":"arxiv","version":1}},"canonical_sha256":"e187b9ff15d0f0ef5d65fe200e4bafb432afa27db1a27bc1a44cb7dd78891ffc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e187b9ff15d0f0ef5d65fe200e4bafb432afa27db1a27bc1a44cb7dd78891ffc","first_computed_at":"2026-05-17T23:51:16.083908Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:16.083908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8fQtKHEcPSy4n/FUQt87S3EBPoaW+9U8H9EVM2U5s6JkSDHCBzHrD/byWgo9O1l+7my/PqjZr0xwgtTG49MdDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:16.084406Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.05820","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c184a7de00dfcb40f4e31927cf83a36fb7ca5a17e05007cf209aedf273cf0f4","sha256:d917f22e54f1ee897f22dcf2603fc7244c205b8024fdb6d625bd22e65ef42e54"],"state_sha256":"0b193431207766d89b2bce903b31a18df3942ba0aa3b68bdfd12472e12eba09e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MrS199FUihe9fRrp2YmEQs7Tk32EFHpb0wEoT2B6X9rEbKu4Z/gRSL8uS7WwFPTNL/IL6xFP/azPM3riTzHtCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T08:07:35.665361Z","bundle_sha256":"51a7181861a47e205cd4554738f819b4262c41af21b8971570f02d38ec4c294b"}}