{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SFMDI4E7EIXNN4B3HGM3GXIRQX","short_pith_number":"pith:SFMDI4E7","schema_version":"1.0","canonical_sha256":"915834709f222ed6f03b3999b35d1185f03c4dec558621fac86e3951cd41e00a","source":{"kind":"arxiv","id":"1611.01455","version":1},"attestation_state":"computed","paper":{"title":"Ways of Conditioning Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Byoung-Tak Zhang, Hanock Kwak","submitted_at":"2016-11-04T17:08:54Z","abstract_excerpt":"The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cros"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1611.01455","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-04T17:08:54Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"775aab0222ca331ac7f7f16a1c187a1a4c5f6c73a83490447e1a7f2c18e21b6e","abstract_canon_sha256":"96cb086d8b170f75c8dfbd228b95e94eda2d7bf5121cfcfab42b19100b43acfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:10.960969Z","signature_b64":"vJG1KDUYO8awjtor1xM621g6IEvyO012QyanLkOtujTAZa+Obp1FUR9Rd/oe5vPsr9ctLHSSf+Xd6FX+KfeMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"915834709f222ed6f03b3999b35d1185f03c4dec558621fac86e3951cd41e00a","last_reissued_at":"2026-05-18T01:00:10.960211Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:10.960211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ways of Conditioning Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Byoung-Tak Zhang, Hanock Kwak","submitted_at":"2016-11-04T17:08:54Z","abstract_excerpt":"The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cros"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01455","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.01455","created_at":"2026-05-18T01:00:10.960344+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.01455v1","created_at":"2026-05-18T01:00:10.960344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01455","created_at":"2026-05-18T01:00:10.960344+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFMDI4E7EIXN","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFMDI4E7EIXNN4B3","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFMDI4E7","created_at":"2026-05-18T12:30:44.179134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX","json":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX.json","graph_json":"https://pith.science/api/pith-number/SFMDI4E7EIXNN4B3HGM3GXIRQX/graph.json","events_json":"https://pith.science/api/pith-number/SFMDI4E7EIXNN4B3HGM3GXIRQX/events.json","paper":"https://pith.science/paper/SFMDI4E7"},"agent_actions":{"view_html":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX","download_json":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX.json","view_paper":"https://pith.science/paper/SFMDI4E7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.01455&json=true","fetch_graph":"https://pith.science/api/pith-number/SFMDI4E7EIXNN4B3HGM3GXIRQX/graph.json","fetch_events":"https://pith.science/api/pith-number/SFMDI4E7EIXNN4B3HGM3GXIRQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX/action/storage_attestation","attest_author":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX/action/author_attestation","sign_citation":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX/action/citation_signature","submit_replication":"https://pith.science/pith/SFMDI4E7EIXNN4B3HGM3GXIRQX/action/replication_record"}},"created_at":"2026-05-18T01:00:10.960344+00:00","updated_at":"2026-05-18T01:00:10.960344+00:00"}