{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DYNXOG746S3OA2QBDK6KVFHYCT","short_pith_number":"pith:DYNXOG74","schema_version":"1.0","canonical_sha256":"1e1b771bfcf4b6e06a011abcaa94f814d0a71208986459f0c34b7f1070a3244d","source":{"kind":"arxiv","id":"1807.11346","version":2},"attestation_state":"computed","paper":{"title":"Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christoph Meinel, Gon\\c{c}alo Mordido, Haojin Yang","submitted_at":"2018-07-30T13:45:16Z","abstract_excerpt":"We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We f"},"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":"1807.11346","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-30T13:45:16Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4dfc7e87224099588b06294b338d7ad29e3a93173d329f775f47b230461a62b5","abstract_canon_sha256":"83cab4c095a2491175aaa16c980140e9b855632a5f762cfbadc5eba962ae066d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:34:13.082156Z","signature_b64":"kLP8YO5g0Ie/47XGE4tQKFIJ6NgFtPdfNRo3Rf6u5zSRKMg1WGeCVSiKHQBnNjlkk6Rm/fuhySAUyvf+iCKKBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e1b771bfcf4b6e06a011abcaa94f814d0a71208986459f0c34b7f1070a3244d","last_reissued_at":"2026-07-05T00:34:13.081727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:34:13.081727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christoph Meinel, Gon\\c{c}alo Mordido, Haojin Yang","submitted_at":"2018-07-30T13:45:16Z","abstract_excerpt":"We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.11346","kind":"arxiv","version":2},"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/1807.11346/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":"1807.11346","created_at":"2026-07-05T00:34:13.081785+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.11346v2","created_at":"2026-07-05T00:34:13.081785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.11346","created_at":"2026-07-05T00:34:13.081785+00:00"},{"alias_kind":"pith_short_12","alias_value":"DYNXOG746S3O","created_at":"2026-07-05T00:34:13.081785+00:00"},{"alias_kind":"pith_short_16","alias_value":"DYNXOG746S3OA2QB","created_at":"2026-07-05T00:34:13.081785+00:00"},{"alias_kind":"pith_short_8","alias_value":"DYNXOG74","created_at":"2026-07-05T00:34:13.081785+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04722","citing_title":"Multi-View Face and Gesture Animation with Dynamic Gaussians","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT","json":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT.json","graph_json":"https://pith.science/api/pith-number/DYNXOG746S3OA2QBDK6KVFHYCT/graph.json","events_json":"https://pith.science/api/pith-number/DYNXOG746S3OA2QBDK6KVFHYCT/events.json","paper":"https://pith.science/paper/DYNXOG74"},"agent_actions":{"view_html":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT","download_json":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT.json","view_paper":"https://pith.science/paper/DYNXOG74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.11346&json=true","fetch_graph":"https://pith.science/api/pith-number/DYNXOG746S3OA2QBDK6KVFHYCT/graph.json","fetch_events":"https://pith.science/api/pith-number/DYNXOG746S3OA2QBDK6KVFHYCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT/action/storage_attestation","attest_author":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT/action/author_attestation","sign_citation":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT/action/citation_signature","submit_replication":"https://pith.science/pith/DYNXOG746S3OA2QBDK6KVFHYCT/action/replication_record"}},"created_at":"2026-07-05T00:34:13.081785+00:00","updated_at":"2026-07-05T00:34:13.081785+00:00"}