{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QAD43EFF5IQZJHFB4XZRBM2BUF","short_pith_number":"pith:QAD43EFF","schema_version":"1.0","canonical_sha256":"8007cd90a5ea21949ca1e5f310b341a153cc245e8f02a8bd791699ac96c6351e","source":{"kind":"arxiv","id":"1911.00888","version":1},"attestation_state":"computed","paper":{"title":"Multi-marginal Wasserstein GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chunhua Shen, Jiezhang Cao, Kui Jia, Langyuan Mo, Mingkui Tan, Yifan Zhang","submitted_at":"2019-11-03T13:47:19Z","abstract_excerpt":"Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance among different domains is very intractable; (ii) It is very difficult to exploit cross-domain correlations to match the target domain distributions. In this paper, we propose a novel Multi-marginal Wasserstein GAN (MWGAN) to minimize Wasserstein distance among domains. Specifically,"},"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":"1911.00888","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-03T13:47:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"87d557451e051c0492b1b9999d90789f8c55afe4278f74f080ab102f8fc73a53","abstract_canon_sha256":"dec31cea55c9b47a7e561c9c8104e881880de6b9666eebf753941ca068290c63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:16:43.699541Z","signature_b64":"SL9Yxfnl1qn5F4a1tTWvSbFCbt0HnO4N5BMVC1NPXCwg8gKCikLrEPwH4A2IxQuzB6xSkiFBzGxuKw6izYYHAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8007cd90a5ea21949ca1e5f310b341a153cc245e8f02a8bd791699ac96c6351e","last_reissued_at":"2026-07-05T00:16:43.699113Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:16:43.699113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-marginal Wasserstein GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chunhua Shen, Jiezhang Cao, Kui Jia, Langyuan Mo, Mingkui Tan, Yifan Zhang","submitted_at":"2019-11-03T13:47:19Z","abstract_excerpt":"Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance among different domains is very intractable; (ii) It is very difficult to exploit cross-domain correlations to match the target domain distributions. In this paper, we propose a novel Multi-marginal Wasserstein GAN (MWGAN) to minimize Wasserstein distance among domains. Specifically,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.00888","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/1911.00888/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":"1911.00888","created_at":"2026-07-05T00:16:43.699179+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.00888v1","created_at":"2026-07-05T00:16:43.699179+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.00888","created_at":"2026-07-05T00:16:43.699179+00:00"},{"alias_kind":"pith_short_12","alias_value":"QAD43EFF5IQZ","created_at":"2026-07-05T00:16:43.699179+00:00"},{"alias_kind":"pith_short_16","alias_value":"QAD43EFF5IQZJHFB","created_at":"2026-07-05T00:16:43.699179+00:00"},{"alias_kind":"pith_short_8","alias_value":"QAD43EFF","created_at":"2026-07-05T00:16:43.699179+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/QAD43EFF5IQZJHFB4XZRBM2BUF","json":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF.json","graph_json":"https://pith.science/api/pith-number/QAD43EFF5IQZJHFB4XZRBM2BUF/graph.json","events_json":"https://pith.science/api/pith-number/QAD43EFF5IQZJHFB4XZRBM2BUF/events.json","paper":"https://pith.science/paper/QAD43EFF"},"agent_actions":{"view_html":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF","download_json":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF.json","view_paper":"https://pith.science/paper/QAD43EFF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.00888&json=true","fetch_graph":"https://pith.science/api/pith-number/QAD43EFF5IQZJHFB4XZRBM2BUF/graph.json","fetch_events":"https://pith.science/api/pith-number/QAD43EFF5IQZJHFB4XZRBM2BUF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF/action/storage_attestation","attest_author":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF/action/author_attestation","sign_citation":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF/action/citation_signature","submit_replication":"https://pith.science/pith/QAD43EFF5IQZJHFB4XZRBM2BUF/action/replication_record"}},"created_at":"2026-07-05T00:16:43.699179+00:00","updated_at":"2026-07-05T00:16:43.699179+00:00"}