{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:NPOWITF753YPTPHIJBWQXG6TW2","short_pith_number":"pith:NPOWITF7","canonical_record":{"source":{"id":"1611.01331","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-04T11:20:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e41de40797c52277113164a147e9a2735db33d004d03f1902c8064fee3d07f0a","abstract_canon_sha256":"016854d4095ee3bbeef89737535f4509a4af02db9acc63c376efd8df3080446b"},"schema_version":"1.0"},"canonical_sha256":"6bdd644cbfeef0f9bce8486d0b9bd3b682fd4467a386b2c88d1f274be6a19087","source":{"kind":"arxiv","id":"1611.01331","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01331","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01331v5","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01331","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"pith_short_12","alias_value":"NPOWITF753YP","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NPOWITF753YPTPHI","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NPOWITF7","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:NPOWITF753YPTPHIJBWQXG6TW2","target":"record","payload":{"canonical_record":{"source":{"id":"1611.01331","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-04T11:20:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e41de40797c52277113164a147e9a2735db33d004d03f1902c8064fee3d07f0a","abstract_canon_sha256":"016854d4095ee3bbeef89737535f4509a4af02db9acc63c376efd8df3080446b"},"schema_version":"1.0"},"canonical_sha256":"6bdd644cbfeef0f9bce8486d0b9bd3b682fd4467a386b2c88d1f274be6a19087","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:58.490262Z","signature_b64":"cIAonY/aVwiQGaUNsFzoo2pXqXGJeTZA2G9/QRX5ssBACFbutXCyzDkMlufseA9gYV5Rq95iSO3emYzq4O9wCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bdd644cbfeef0f9bce8486d0b9bd3b682fd4467a386b2c88d1f274be6a19087","last_reissued_at":"2026-05-18T00:52:58.489826Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:58.489826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.01331","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:52:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3i1pnUsaAHsIvzeJ9i4t6BHYII78NyyPt0dATikCD29ovuaWzoQugOdf1fFjr3WT+DIatVr2ZsS657Zz2YnoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:48:42.382154Z"},"content_sha256":"b1edd76300a71fc9a889fd431390b1b1b9f7a482e78099e86b4a76b28c02d853","schema_version":"1.0","event_id":"sha256:b1edd76300a71fc9a889fd431390b1b1b9f7a482e78099e86b4a76b28c02d853"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:NPOWITF753YPTPHIJBWQXG6TW2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RenderGAN: Generating Realistic Labeled Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Benjamin Wild, Leon Sixt, Tim Landgraf","submitted_at":"2016-11-04T11:20:38Z","abstract_excerpt":"Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01331","kind":"arxiv","version":5},"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":"2026-05-18T00:52:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s1+834nIC/m7F6Exo4M+XMYDy+rlfqh3ZM+25b7tjpMZHY6Uw9vEUreRPiRODW/P3yecYl1R84jKijhpTWT7AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:48:42.382588Z"},"content_sha256":"b47e744c449bf6f35ea360d740d248b369d15c56d052614442bbb275ba07939b","schema_version":"1.0","event_id":"sha256:b47e744c449bf6f35ea360d740d248b369d15c56d052614442bbb275ba07939b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NPOWITF753YPTPHIJBWQXG6TW2/bundle.json","state_url":"https://pith.science/pith/NPOWITF753YPTPHIJBWQXG6TW2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NPOWITF753YPTPHIJBWQXG6TW2/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":"2026-05-26T06:48:42Z","links":{"resolver":"https://pith.science/pith/NPOWITF753YPTPHIJBWQXG6TW2","bundle":"https://pith.science/pith/NPOWITF753YPTPHIJBWQXG6TW2/bundle.json","state":"https://pith.science/pith/NPOWITF753YPTPHIJBWQXG6TW2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NPOWITF753YPTPHIJBWQXG6TW2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:NPOWITF753YPTPHIJBWQXG6TW2","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":"016854d4095ee3bbeef89737535f4509a4af02db9acc63c376efd8df3080446b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-04T11:20:38Z","title_canon_sha256":"e41de40797c52277113164a147e9a2735db33d004d03f1902c8064fee3d07f0a"},"schema_version":"1.0","source":{"id":"1611.01331","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01331","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01331v5","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01331","created_at":"2026-05-18T00:52:58Z"},{"alias_kind":"pith_short_12","alias_value":"NPOWITF753YP","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NPOWITF753YPTPHI","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NPOWITF7","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:b47e744c449bf6f35ea360d740d248b369d15c56d052614442bbb275ba07939b","target":"graph","created_at":"2026-05-18T00:52:58Z","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":"Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the","authors_text":"Benjamin Wild, Leon Sixt, Tim Landgraf","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-04T11:20:38Z","title":"RenderGAN: Generating Realistic Labeled Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01331","kind":"arxiv","version":5},"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:b1edd76300a71fc9a889fd431390b1b1b9f7a482e78099e86b4a76b28c02d853","target":"record","created_at":"2026-05-18T00:52:58Z","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":"016854d4095ee3bbeef89737535f4509a4af02db9acc63c376efd8df3080446b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-04T11:20:38Z","title_canon_sha256":"e41de40797c52277113164a147e9a2735db33d004d03f1902c8064fee3d07f0a"},"schema_version":"1.0","source":{"id":"1611.01331","kind":"arxiv","version":5}},"canonical_sha256":"6bdd644cbfeef0f9bce8486d0b9bd3b682fd4467a386b2c88d1f274be6a19087","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6bdd644cbfeef0f9bce8486d0b9bd3b682fd4467a386b2c88d1f274be6a19087","first_computed_at":"2026-05-18T00:52:58.489826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:58.489826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cIAonY/aVwiQGaUNsFzoo2pXqXGJeTZA2G9/QRX5ssBACFbutXCyzDkMlufseA9gYV5Rq95iSO3emYzq4O9wCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:58.490262Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.01331","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1edd76300a71fc9a889fd431390b1b1b9f7a482e78099e86b4a76b28c02d853","sha256:b47e744c449bf6f35ea360d740d248b369d15c56d052614442bbb275ba07939b"],"state_sha256":"cdce52d8d30548bf221733761dc3f508cd47ade620f7305cb6d65c19b940dee8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"82Eg0Xh1ot1hdeG6TG9Pt0xiyKXtDrzdZ7dHiqbdJ4tM5XqujxDo7ywe5q/qv5Wk7XaWxTjJXS86L45UEbZ3Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T06:48:42.386736Z","bundle_sha256":"322de7e98b720827bfe8dc5f3a2944b9c86dc6b502dc88d13418b8511d293418"}}