{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5GGMRVUWAULK5WBU66IMYPWD5R","short_pith_number":"pith:5GGMRVUW","canonical_record":{"source":{"id":"1805.08657","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T15:14:11Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"41fb0977e13e2a4822d0de02ab9553fc234e06db5ddf08ae392c82c0dbe48bc0","abstract_canon_sha256":"e4a56cde11eeaa199fa94699e316bd05a9e366dd06b33e109f759f3b62f866e8"},"schema_version":"1.0"},"canonical_sha256":"e98cc8d6960516aed834f790cc3ec3ec57c01ab262f388d6acff273a3e4c3f9c","source":{"kind":"arxiv","id":"1805.08657","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08657","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08657v2","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08657","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"5GGMRVUWAULK","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5GGMRVUWAULK5WBU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5GGMRVUW","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5GGMRVUWAULK5WBU66IMYPWD5R","target":"record","payload":{"canonical_record":{"source":{"id":"1805.08657","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T15:14:11Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"41fb0977e13e2a4822d0de02ab9553fc234e06db5ddf08ae392c82c0dbe48bc0","abstract_canon_sha256":"e4a56cde11eeaa199fa94699e316bd05a9e366dd06b33e109f759f3b62f866e8"},"schema_version":"1.0"},"canonical_sha256":"e98cc8d6960516aed834f790cc3ec3ec57c01ab262f388d6acff273a3e4c3f9c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:26.083300Z","signature_b64":"Cu+XpE1kQc8zCocweVnFT6hir3PYdMEpzOya8rsPACOTpNA26/8cOmu9m7GOgqd5ft5qzoUnMc/XFVx9gvvnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e98cc8d6960516aed834f790cc3ec3ec57c01ab262f388d6acff273a3e4c3f9c","last_reissued_at":"2026-05-17T23:51:26.082657Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:26.082657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.08657","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":"2026-05-17T23:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FAgTV1/sNdVZ052K8sToG2POxJG+xYd/YgoQTkz5SGOR0IGn1RhhOioK6JM97KIF8uDPrfTCHZ+6p0LoUeJvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:42:07.504611Z"},"content_sha256":"f7f696b7188c8ef91978d1827ce97c21712189c65e61d1bb9414dc7cf8270800","schema_version":"1.0","event_id":"sha256:f7f696b7188c8ef91978d1827ce97c21712189c65e61d1bb9414dc7cf8270800"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5GGMRVUWAULK5WBU66IMYPWD5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Conditional Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou","submitted_at":"2018-05-22T15:14:11Z","abstract_excerpt":"Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise. The regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGAN unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08657","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":""},"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":"2026-05-17T23:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PV8qgfUUE03pMUiZTY8/YI597DU13L1iSG0If/rDo1WVulw8FaWOnz0kFWmm+062mjlBWYImtyUhEbw4hLSiBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:42:07.505341Z"},"content_sha256":"3b731beecb1b23cb44bd733dbf4071a4f00cbadcb8d3ac50aa10600f2c33c281","schema_version":"1.0","event_id":"sha256:3b731beecb1b23cb44bd733dbf4071a4f00cbadcb8d3ac50aa10600f2c33c281"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5GGMRVUWAULK5WBU66IMYPWD5R/bundle.json","state_url":"https://pith.science/pith/5GGMRVUWAULK5WBU66IMYPWD5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5GGMRVUWAULK5WBU66IMYPWD5R/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":"2026-06-11T21:42:07Z","links":{"resolver":"https://pith.science/pith/5GGMRVUWAULK5WBU66IMYPWD5R","bundle":"https://pith.science/pith/5GGMRVUWAULK5WBU66IMYPWD5R/bundle.json","state":"https://pith.science/pith/5GGMRVUWAULK5WBU66IMYPWD5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5GGMRVUWAULK5WBU66IMYPWD5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5GGMRVUWAULK5WBU66IMYPWD5R","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":"e4a56cde11eeaa199fa94699e316bd05a9e366dd06b33e109f759f3b62f866e8","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T15:14:11Z","title_canon_sha256":"41fb0977e13e2a4822d0de02ab9553fc234e06db5ddf08ae392c82c0dbe48bc0"},"schema_version":"1.0","source":{"id":"1805.08657","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08657","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08657v2","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08657","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"5GGMRVUWAULK","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5GGMRVUWAULK5WBU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5GGMRVUW","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:3b731beecb1b23cb44bd733dbf4071a4f00cbadcb8d3ac50aa10600f2c33c281","target":"graph","created_at":"2026-05-17T23:51:26Z","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":"Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise. The regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGAN unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue","authors_text":"Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T15:14:11Z","title":"Robust Conditional Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08657","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:f7f696b7188c8ef91978d1827ce97c21712189c65e61d1bb9414dc7cf8270800","target":"record","created_at":"2026-05-17T23:51:26Z","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":"e4a56cde11eeaa199fa94699e316bd05a9e366dd06b33e109f759f3b62f866e8","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T15:14:11Z","title_canon_sha256":"41fb0977e13e2a4822d0de02ab9553fc234e06db5ddf08ae392c82c0dbe48bc0"},"schema_version":"1.0","source":{"id":"1805.08657","kind":"arxiv","version":2}},"canonical_sha256":"e98cc8d6960516aed834f790cc3ec3ec57c01ab262f388d6acff273a3e4c3f9c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e98cc8d6960516aed834f790cc3ec3ec57c01ab262f388d6acff273a3e4c3f9c","first_computed_at":"2026-05-17T23:51:26.082657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:26.082657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Cu+XpE1kQc8zCocweVnFT6hir3PYdMEpzOya8rsPACOTpNA26/8cOmu9m7GOgqd5ft5qzoUnMc/XFVx9gvvnCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:26.083300Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.08657","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f7f696b7188c8ef91978d1827ce97c21712189c65e61d1bb9414dc7cf8270800","sha256:3b731beecb1b23cb44bd733dbf4071a4f00cbadcb8d3ac50aa10600f2c33c281"],"state_sha256":"411ef2fc6463077f70873fe91d0fe1bb55f4a86d9ac86ad29fa73c1a97fb55a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XpDAtwmSGAK2EsRwgcHV35+YK2JwQgqJMJgRY200aYPzR2rfuw+hbx7XEV0zxhy8HgI+BuvHDOcwNk1f1YSeBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T21:42:07.509131Z","bundle_sha256":"47faa6fd55f4b0e1f5aa0580d9595463191c476f448c311d3c0ff55a0cbc668c"}}