{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:35SU6CZCJREEO6TYMWBVVYCYGF","short_pith_number":"pith:35SU6CZC","canonical_record":{"source":{"id":"2010.12622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-23T19:13:44Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"6a3b178c5d75713ae41b65e6c2160f97ec7ddee995ba879c84e8a95bc7dd2e3d","abstract_canon_sha256":"3e05ec4fd5e9a79e70fa410080c9eaa006d3d7896ca7606fa20efbf0b5c7555f"},"schema_version":"1.0"},"canonical_sha256":"df654f0b224c48477a7865835ae058315799e94421cec0243e1449dfffdfac98","source":{"kind":"arxiv","id":"2010.12622","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.12622","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"arxiv_version","alias_value":"2010.12622v1","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.12622","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_12","alias_value":"35SU6CZCJREE","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_16","alias_value":"35SU6CZCJREEO6TY","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_8","alias_value":"35SU6CZC","created_at":"2026-07-05T01:45:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:35SU6CZCJREEO6TYMWBVVYCYGF","target":"record","payload":{"canonical_record":{"source":{"id":"2010.12622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-23T19:13:44Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"6a3b178c5d75713ae41b65e6c2160f97ec7ddee995ba879c84e8a95bc7dd2e3d","abstract_canon_sha256":"3e05ec4fd5e9a79e70fa410080c9eaa006d3d7896ca7606fa20efbf0b5c7555f"},"schema_version":"1.0"},"canonical_sha256":"df654f0b224c48477a7865835ae058315799e94421cec0243e1449dfffdfac98","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:46.562652Z","signature_b64":"maaEK8muvypW9nYhRsCBl62+Ht6N7oSL5Ov2c8Kvu5tSVqHui+F5aikqxoIckFWKXvnoA67584riUUR/gxPmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df654f0b224c48477a7865835ae058315799e94421cec0243e1449dfffdfac98","last_reissued_at":"2026-07-05T01:45:46.562265Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:46.562265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.12622","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":"2026-07-05T01:45:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v18jQuKfBMqK6e+aOgfb9HZ+G5bv2tb7p52OCkldG31MV64Ed4XQfitElMXmISEcg69mJ5oRyRmPNL7tjZwUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:43:39.621302Z"},"content_sha256":"b101cb4665326e5ad507acfb35b41eb1948f5d3762bd68eeb6ad788a46e64387","schema_version":"1.0","event_id":"sha256:b101cb4665326e5ad507acfb35b41eb1948f5d3762bd68eeb6ad788a46e64387"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:35SU6CZCJREEO6TYMWBVVYCYGF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"S2cGAN: Semi-Supervised Training of Conditional GANs with Fewer Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arunava Chakraborty, Mahir Shah, Nipun Kwatra, Rahul Ragesh","submitted_at":"2020-10-23T19:13:44Z","abstract_excerpt":"Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they provide limited control over the generation process. Conditional GANs (cGANs) provide a mechanism to control the generation process by conditioning the output on a user defined input. Although training GANs requires only unsupervised data, training cGANs requires labelled data which can be very expensive to obtain. We propose a framework for semi-supervised training of cGANs which utilizes sparse labels to learn the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.12622","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/2010.12622/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":"2026-07-05T01:45:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2BIDu2mEDdw+wKsmxcyUvtBAIhfvFNHzjvNTOZQrPJWld5okHFps6Ojfj6+/GJYqKnyC16CCbeX8mg2BdC0YDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:43:39.621973Z"},"content_sha256":"22ee66fe36d26353cb8c876ca50473e783e123b2007c9193b5333fbfa7fc368c","schema_version":"1.0","event_id":"sha256:22ee66fe36d26353cb8c876ca50473e783e123b2007c9193b5333fbfa7fc368c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/35SU6CZCJREEO6TYMWBVVYCYGF/bundle.json","state_url":"https://pith.science/pith/35SU6CZCJREEO6TYMWBVVYCYGF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/35SU6CZCJREEO6TYMWBVVYCYGF/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":"2026-07-09T05:43:39Z","links":{"resolver":"https://pith.science/pith/35SU6CZCJREEO6TYMWBVVYCYGF","bundle":"https://pith.science/pith/35SU6CZCJREEO6TYMWBVVYCYGF/bundle.json","state":"https://pith.science/pith/35SU6CZCJREEO6TYMWBVVYCYGF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/35SU6CZCJREEO6TYMWBVVYCYGF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:35SU6CZCJREEO6TYMWBVVYCYGF","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":"3e05ec4fd5e9a79e70fa410080c9eaa006d3d7896ca7606fa20efbf0b5c7555f","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-23T19:13:44Z","title_canon_sha256":"6a3b178c5d75713ae41b65e6c2160f97ec7ddee995ba879c84e8a95bc7dd2e3d"},"schema_version":"1.0","source":{"id":"2010.12622","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.12622","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"arxiv_version","alias_value":"2010.12622v1","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.12622","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_12","alias_value":"35SU6CZCJREE","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_16","alias_value":"35SU6CZCJREEO6TY","created_at":"2026-07-05T01:45:46Z"},{"alias_kind":"pith_short_8","alias_value":"35SU6CZC","created_at":"2026-07-05T01:45:46Z"}],"graph_snapshots":[{"event_id":"sha256:22ee66fe36d26353cb8c876ca50473e783e123b2007c9193b5333fbfa7fc368c","target":"graph","created_at":"2026-07-05T01:45:46Z","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/2010.12622/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they provide limited control over the generation process. Conditional GANs (cGANs) provide a mechanism to control the generation process by conditioning the output on a user defined input. Although training GANs requires only unsupervised data, training cGANs requires labelled data which can be very expensive to obtain. We propose a framework for semi-supervised training of cGANs which utilizes sparse labels to learn the ","authors_text":"Arunava Chakraborty, Mahir Shah, Nipun Kwatra, Rahul Ragesh","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-23T19:13:44Z","title":"S2cGAN: Semi-Supervised Training of Conditional GANs with Fewer Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.12622","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:b101cb4665326e5ad507acfb35b41eb1948f5d3762bd68eeb6ad788a46e64387","target":"record","created_at":"2026-07-05T01:45:46Z","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":"3e05ec4fd5e9a79e70fa410080c9eaa006d3d7896ca7606fa20efbf0b5c7555f","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-23T19:13:44Z","title_canon_sha256":"6a3b178c5d75713ae41b65e6c2160f97ec7ddee995ba879c84e8a95bc7dd2e3d"},"schema_version":"1.0","source":{"id":"2010.12622","kind":"arxiv","version":1}},"canonical_sha256":"df654f0b224c48477a7865835ae058315799e94421cec0243e1449dfffdfac98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df654f0b224c48477a7865835ae058315799e94421cec0243e1449dfffdfac98","first_computed_at":"2026-07-05T01:45:46.562265Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:45:46.562265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"maaEK8muvypW9nYhRsCBl62+Ht6N7oSL5Ov2c8Kvu5tSVqHui+F5aikqxoIckFWKXvnoA67584riUUR/gxPmCw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:45:46.562652Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.12622","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b101cb4665326e5ad507acfb35b41eb1948f5d3762bd68eeb6ad788a46e64387","sha256:22ee66fe36d26353cb8c876ca50473e783e123b2007c9193b5333fbfa7fc368c"],"state_sha256":"bf3bc8fff696fbd30ee3bb49ea436381d71bc295c220461828fc2e152548ffaf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nucj5MME/Kj7r0wG7UF/B1iGiUvNHGlLgE+hUUEn6a3Chmb5LL8tqL9e6xzQmeOgUNuUBJtGOwcfUISFKrrnDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:43:39.625484Z","bundle_sha256":"22f07de05873bfbf24f4062c3754924cec5e5fabb74669304b432807b2e2836e"}}