{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2XJUMWKS75PY4GHV62BO2N3QXT","short_pith_number":"pith:2XJUMWKS","schema_version":"1.0","canonical_sha256":"d5d3465952ff5f8e18f5f682ed3770bcca5a9015a52d577a62cb3795c572ec0b","source":{"kind":"arxiv","id":"2005.13178","version":1},"attestation_state":"computed","paper":{"title":"Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Abdolah Chalechale, Maryam Taghizadeh, Pegah Salehi","submitted_at":"2020-05-27T05:56:53Z","abstract_excerpt":"One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density funct"},"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":"2005.13178","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-27T05:56:53Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"99dafa4243d6eafe7808c66ed8819296001767a29efdf2199177eeda0abf573e","abstract_canon_sha256":"b0cde1af098b2a182745ff516b4e0d2d83a4596923187cb69d035c97075e04d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:06:13.665138Z","signature_b64":"eV9bmDoHv0IrklyHP6WWLTjHJ2Fhqfee6orcpCw1hjTj1gKtE38W2z/t9ZuO+AejtEJFah4kDgrCogInXGXFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5d3465952ff5f8e18f5f682ed3770bcca5a9015a52d577a62cb3795c572ec0b","last_reissued_at":"2026-07-05T01:06:13.664745Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:06:13.664745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Abdolah Chalechale, Maryam Taghizadeh, Pegah Salehi","submitted_at":"2020-05-27T05:56:53Z","abstract_excerpt":"One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density funct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.13178","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/2005.13178/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.13178","created_at":"2026-07-05T01:06:13.664807+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.13178v1","created_at":"2026-07-05T01:06:13.664807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.13178","created_at":"2026-07-05T01:06:13.664807+00:00"},{"alias_kind":"pith_short_12","alias_value":"2XJUMWKS75PY","created_at":"2026-07-05T01:06:13.664807+00:00"},{"alias_kind":"pith_short_16","alias_value":"2XJUMWKS75PY4GHV","created_at":"2026-07-05T01:06:13.664807+00:00"},{"alias_kind":"pith_short_8","alias_value":"2XJUMWKS","created_at":"2026-07-05T01:06:13.664807+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02589","citing_title":"Representation learning from OCT images","ref_index":119,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT","json":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT.json","graph_json":"https://pith.science/api/pith-number/2XJUMWKS75PY4GHV62BO2N3QXT/graph.json","events_json":"https://pith.science/api/pith-number/2XJUMWKS75PY4GHV62BO2N3QXT/events.json","paper":"https://pith.science/paper/2XJUMWKS"},"agent_actions":{"view_html":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT","download_json":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT.json","view_paper":"https://pith.science/paper/2XJUMWKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.13178&json=true","fetch_graph":"https://pith.science/api/pith-number/2XJUMWKS75PY4GHV62BO2N3QXT/graph.json","fetch_events":"https://pith.science/api/pith-number/2XJUMWKS75PY4GHV62BO2N3QXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT/action/storage_attestation","attest_author":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT/action/author_attestation","sign_citation":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT/action/citation_signature","submit_replication":"https://pith.science/pith/2XJUMWKS75PY4GHV62BO2N3QXT/action/replication_record"}},"created_at":"2026-07-05T01:06:13.664807+00:00","updated_at":"2026-07-05T01:06:13.664807+00:00"}