{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:NQIRO3F55V6ZC46U3Z5PWWPRV7","short_pith_number":"pith:NQIRO3F5","canonical_record":{"source":{"id":"1706.05477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-17T05:29:13Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"182009af44d185e7eefad12414dbf93bf7407ca2171e4ae623aefb1e8232c3f7","abstract_canon_sha256":"a7d3c8296537808974e655ddd9b3e183d064460f5516b43864146fd679bb1c80"},"schema_version":"1.0"},"canonical_sha256":"6c11176cbded7d9173d4de7afb59f1afcb47100e1bca92c49d23fe5d901b269c","source":{"kind":"arxiv","id":"1706.05477","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05477","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05477v1","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05477","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"pith_short_12","alias_value":"NQIRO3F55V6Z","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NQIRO3F55V6ZC46U","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NQIRO3F5","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:NQIRO3F55V6ZC46U3Z5PWWPRV7","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-17T05:29:13Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"182009af44d185e7eefad12414dbf93bf7407ca2171e4ae623aefb1e8232c3f7","abstract_canon_sha256":"a7d3c8296537808974e655ddd9b3e183d064460f5516b43864146fd679bb1c80"},"schema_version":"1.0"},"canonical_sha256":"6c11176cbded7d9173d4de7afb59f1afcb47100e1bca92c49d23fe5d901b269c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:11.540456Z","signature_b64":"bJIqw77Xq5ti6IaK++WeRTtaFZaDil2kWxEFvMSffBot4m/QR90rp74PV71ipEMEyKxI9NmYS8LIGLg4DP1TDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c11176cbded7d9173d4de7afb59f1afcb47100e1bca92c49d23fe5d901b269c","last_reissued_at":"2026-05-18T00:42:11.539915Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:11.539915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05477","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":"2026-05-18T00:42:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4xOiTwIGHTDpNfdwQE2Id8AD4S/cHn4A2C8lLtaEHZPXog+8AkkLG5pgpkr2YUqXS42bxQ2BAesyuot9/LTyDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:25:16.993302Z"},"content_sha256":"11b7d45471a906f4586e3d3257f49f11553a422809e1b61eb8b1cad04c32860d","schema_version":"1.0","event_id":"sha256:11b7d45471a906f4586e3d3257f49f11553a422809e1b61eb8b1cad04c32860d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:NQIRO3F55V6ZC46U3Z5PWWPRV7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Conditional Generative Adverserial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anthony Dick, Anton van den Hengel, Iman Abbasnejad, M. Ehsan Abbasnejad, Qinfeng Shi","submitted_at":"2017-06-17T05:29:13Z","abstract_excerpt":"Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outpe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05477","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":"2026-05-18T00:42:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r4ouec6FlUFPGL+Ahn3e/rf8XkzMxlZkmb3bGziP3Zwu/cIpKojMwZck/wFcaSVfIL4YO+WQgiZT+hNed58oCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:25:16.993667Z"},"content_sha256":"f959e056ec1d9050f2cf7c8fabd9f857056775561f28817fe3bd50827ceb1714","schema_version":"1.0","event_id":"sha256:f959e056ec1d9050f2cf7c8fabd9f857056775561f28817fe3bd50827ceb1714"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/bundle.json","state_url":"https://pith.science/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/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":"2026-06-06T16:25:16Z","links":{"resolver":"https://pith.science/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7","bundle":"https://pith.science/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/bundle.json","state":"https://pith.science/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NQIRO3F55V6ZC46U3Z5PWWPRV7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NQIRO3F55V6ZC46U3Z5PWWPRV7","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":"a7d3c8296537808974e655ddd9b3e183d064460f5516b43864146fd679bb1c80","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-17T05:29:13Z","title_canon_sha256":"182009af44d185e7eefad12414dbf93bf7407ca2171e4ae623aefb1e8232c3f7"},"schema_version":"1.0","source":{"id":"1706.05477","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05477","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05477v1","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05477","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"pith_short_12","alias_value":"NQIRO3F55V6Z","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NQIRO3F55V6ZC46U","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NQIRO3F5","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:f959e056ec1d9050f2cf7c8fabd9f857056775561f28817fe3bd50827ceb1714","target":"graph","created_at":"2026-05-18T00:42:11Z","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":"Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outpe","authors_text":"Anthony Dick, Anton van den Hengel, Iman Abbasnejad, M. Ehsan Abbasnejad, Qinfeng Shi","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-17T05:29:13Z","title":"Bayesian Conditional Generative Adverserial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05477","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":"sha256:11b7d45471a906f4586e3d3257f49f11553a422809e1b61eb8b1cad04c32860d","target":"record","created_at":"2026-05-18T00:42:11Z","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":"a7d3c8296537808974e655ddd9b3e183d064460f5516b43864146fd679bb1c80","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-17T05:29:13Z","title_canon_sha256":"182009af44d185e7eefad12414dbf93bf7407ca2171e4ae623aefb1e8232c3f7"},"schema_version":"1.0","source":{"id":"1706.05477","kind":"arxiv","version":1}},"canonical_sha256":"6c11176cbded7d9173d4de7afb59f1afcb47100e1bca92c49d23fe5d901b269c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c11176cbded7d9173d4de7afb59f1afcb47100e1bca92c49d23fe5d901b269c","first_computed_at":"2026-05-18T00:42:11.539915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:11.539915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bJIqw77Xq5ti6IaK++WeRTtaFZaDil2kWxEFvMSffBot4m/QR90rp74PV71ipEMEyKxI9NmYS8LIGLg4DP1TDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:11.540456Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05477","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11b7d45471a906f4586e3d3257f49f11553a422809e1b61eb8b1cad04c32860d","sha256:f959e056ec1d9050f2cf7c8fabd9f857056775561f28817fe3bd50827ceb1714"],"state_sha256":"7b77870bfa7c3760e78143a061f991bc0af775b2a0874d82e547d86fdd72698f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eqx6W87zUvBqEC2tlMOQqiXz7ALQV7s62ydu4dxHdT2qOkcaOgAZ3dhwt6Q0XPqlsmkQaFHSdTWh59Bo9zx6AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T16:25:16.995836Z","bundle_sha256":"d683f18a515e95924f308da64bf3676c08ced99776f3fa471e0a505fd8a812b5"}}