{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HJ7POLEJL3XFS7HKNHQGBPPDW4","short_pith_number":"pith:HJ7POLEJ","schema_version":"1.0","canonical_sha256":"3a7ef72c895eee597cea69e060bde3b708c54622ce74521e45c80dd5b96d1d18","source":{"kind":"arxiv","id":"1712.02330","version":1},"attestation_state":"computed","paper":{"title":"SGAN: An Alternative Training of Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Fran\\c{c}ois Fleuret, Tatjana Chavdarova","submitted_at":"2017-12-06T18:52:21Z","abstract_excerpt":"The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them.\n  We consider an alternative training process, named SGAN, in which several adversarial \"local\" pairs of networks are trained independently so that a \"global\" supervising pair of networks can be trained against them. The goal is to train the global pair with the correspo"},"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":"1712.02330","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-06T18:52:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67e73b6a179837f361da762c4284362c31a0e9f205d6199537f2564e70366176","abstract_canon_sha256":"c963a39a1a628e0f6508a0d0308c4daee46d5f33a66ea6ef0bfbd0bc660d2fc9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:38.465337Z","signature_b64":"ZMJpQJwDqQPp5lo+puIAOmhXBO2IRpSYST0hC1R9vrWLAF0ug56R6cC7ELrugmqZ39TdS4Xn4Frsl+ReFzsRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a7ef72c895eee597cea69e060bde3b708c54622ce74521e45c80dd5b96d1d18","last_reissued_at":"2026-05-18T00:28:38.464683Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:38.464683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SGAN: An Alternative Training of Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Fran\\c{c}ois Fleuret, Tatjana Chavdarova","submitted_at":"2017-12-06T18:52:21Z","abstract_excerpt":"The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them.\n  We consider an alternative training process, named SGAN, in which several adversarial \"local\" pairs of networks are trained independently so that a \"global\" supervising pair of networks can be trained against them. The goal is to train the global pair with the correspo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.02330","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":"1712.02330","created_at":"2026-05-18T00:28:38.464775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.02330v1","created_at":"2026-05-18T00:28:38.464775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.02330","created_at":"2026-05-18T00:28:38.464775+00:00"},{"alias_kind":"pith_short_12","alias_value":"HJ7POLEJL3XF","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HJ7POLEJL3XFS7HK","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HJ7POLEJ","created_at":"2026-05-18T12:31:18.294218+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/HJ7POLEJL3XFS7HKNHQGBPPDW4","json":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4.json","graph_json":"https://pith.science/api/pith-number/HJ7POLEJL3XFS7HKNHQGBPPDW4/graph.json","events_json":"https://pith.science/api/pith-number/HJ7POLEJL3XFS7HKNHQGBPPDW4/events.json","paper":"https://pith.science/paper/HJ7POLEJ"},"agent_actions":{"view_html":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4","download_json":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4.json","view_paper":"https://pith.science/paper/HJ7POLEJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.02330&json=true","fetch_graph":"https://pith.science/api/pith-number/HJ7POLEJL3XFS7HKNHQGBPPDW4/graph.json","fetch_events":"https://pith.science/api/pith-number/HJ7POLEJL3XFS7HKNHQGBPPDW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4/action/storage_attestation","attest_author":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4/action/author_attestation","sign_citation":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4/action/citation_signature","submit_replication":"https://pith.science/pith/HJ7POLEJL3XFS7HKNHQGBPPDW4/action/replication_record"}},"created_at":"2026-05-18T00:28:38.464775+00:00","updated_at":"2026-05-18T00:28:38.464775+00:00"}