{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7OIKNOZBNOUQVFZ273QCUMDFKI","short_pith_number":"pith:7OIKNOZB","schema_version":"1.0","canonical_sha256":"fb90a6bb216ba90a973afee02a30655220a9d0e3f7480bdbe52c259990f7d0b3","source":{"kind":"arxiv","id":"1806.07751","version":1},"attestation_state":"computed","paper":{"title":"Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Peter Corcoran, Shabab Bazrafkan","submitted_at":"2018-06-19T10:24:38Z","abstract_excerpt":"Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space. In this work, a method known as \"Versatile Auxiliary Classifier with Generative Adversarial Network\" for multi-class scenarios is presented. In this technique, the Generative Adversarial Networks (GAN)'s generator is turned into a conditional generator by placing a multi-class classifier in parallel with the discriminator network and backpropagate the classification error through the generator. This technique is versa"},"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":"1806.07751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-19T10:24:38Z","cross_cats_sorted":["eess.IV","stat.ML"],"title_canon_sha256":"2d36d3a1cf60919fdf4767c0a4c34283ecf52905ce70ebd5233957d75b674b3f","abstract_canon_sha256":"ed83427ee81dc010d001d7b54e04eaf5654484a50c1bfec6d5620a83a4f5b7f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:46.776003Z","signature_b64":"ySxqdiAlEf5x4SQHx6FP5lVLDFCFQ3MJSCw58MesR4+cf8AQArsaMH3K03Hh5cXsBAwmWI6qCJoOMBtumLt3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb90a6bb216ba90a973afee02a30655220a9d0e3f7480bdbe52c259990f7d0b3","last_reissued_at":"2026-05-18T00:12:46.775338Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:46.775338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Peter Corcoran, Shabab Bazrafkan","submitted_at":"2018-06-19T10:24:38Z","abstract_excerpt":"Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space. In this work, a method known as \"Versatile Auxiliary Classifier with Generative Adversarial Network\" for multi-class scenarios is presented. In this technique, the Generative Adversarial Networks (GAN)'s generator is turned into a conditional generator by placing a multi-class classifier in parallel with the discriminator network and backpropagate the classification error through the generator. This technique is versa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07751","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":"1806.07751","created_at":"2026-05-18T00:12:46.775437+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.07751v1","created_at":"2026-05-18T00:12:46.775437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07751","created_at":"2026-05-18T00:12:46.775437+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OIKNOZBNOUQ","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OIKNOZBNOUQVFZ2","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OIKNOZB","created_at":"2026-05-18T12:32:11.075285+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/7OIKNOZBNOUQVFZ273QCUMDFKI","json":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI.json","graph_json":"https://pith.science/api/pith-number/7OIKNOZBNOUQVFZ273QCUMDFKI/graph.json","events_json":"https://pith.science/api/pith-number/7OIKNOZBNOUQVFZ273QCUMDFKI/events.json","paper":"https://pith.science/paper/7OIKNOZB"},"agent_actions":{"view_html":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI","download_json":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI.json","view_paper":"https://pith.science/paper/7OIKNOZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.07751&json=true","fetch_graph":"https://pith.science/api/pith-number/7OIKNOZBNOUQVFZ273QCUMDFKI/graph.json","fetch_events":"https://pith.science/api/pith-number/7OIKNOZBNOUQVFZ273QCUMDFKI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI/action/storage_attestation","attest_author":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI/action/author_attestation","sign_citation":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI/action/citation_signature","submit_replication":"https://pith.science/pith/7OIKNOZBNOUQVFZ273QCUMDFKI/action/replication_record"}},"created_at":"2026-05-18T00:12:46.775437+00:00","updated_at":"2026-05-18T00:12:46.775437+00:00"}