{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YTWJF3H5KXYRTZBDQK5VGCB2JV","short_pith_number":"pith:YTWJF3H5","schema_version":"1.0","canonical_sha256":"c4ec92ecfd55f119e42382bb53083a4d6cfe64f4eab37660ccf96c91790d90e9","source":{"kind":"arxiv","id":"1805.07674","version":3},"attestation_state":"computed","paper":{"title":"BourGAN: Generative Networks with Metric Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chang Xiao, Changxi Zheng, Peilin Zhong","submitted_at":"2018-05-19T23:17:18Z","abstract_excerpt":"This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometric lens, we embed subsamples of the dataset from an arbitrary metric space into the l2 space, while preserving their pairwise distance distribution. Not only does this metric embedding determine the dimensionality of the latent space automatically, it also enables us to construct a mixture of Gaussians to draw latent space random vectors. We use the Gaussian mixture model in tandem with a simple augmentation of the o"},"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":"1805.07674","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-19T23:17:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"df3ae5c2eae5b84f52d9e6669f5fea4e918ef861ff93e5dcb5a955a6a42532a2","abstract_canon_sha256":"08559459d6f60eee912482b3d14a047c57bbafc99353316a4d2cab4250537299"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:43.250960Z","signature_b64":"TdW5JqANl17wFVwr0hE5/Elg5rpp4LwWBfA2PmiSJ/A/SO5U0rXJCM97PAdJoZVNhGlrmGl6+kj5FtpLpWQHBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4ec92ecfd55f119e42382bb53083a4d6cfe64f4eab37660ccf96c91790d90e9","last_reissued_at":"2026-05-17T23:43:43.250289Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:43.250289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BourGAN: Generative Networks with Metric Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chang Xiao, Changxi Zheng, Peilin Zhong","submitted_at":"2018-05-19T23:17:18Z","abstract_excerpt":"This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometric lens, we embed subsamples of the dataset from an arbitrary metric space into the l2 space, while preserving their pairwise distance distribution. Not only does this metric embedding determine the dimensionality of the latent space automatically, it also enables us to construct a mixture of Gaussians to draw latent space random vectors. We use the Gaussian mixture model in tandem with a simple augmentation of the o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07674","kind":"arxiv","version":3},"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":"1805.07674","created_at":"2026-05-17T23:43:43.250428+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.07674v3","created_at":"2026-05-17T23:43:43.250428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07674","created_at":"2026-05-17T23:43:43.250428+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTWJF3H5KXYR","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTWJF3H5KXYRTZBD","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTWJF3H5","created_at":"2026-05-18T12:33:04.347982+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/YTWJF3H5KXYRTZBDQK5VGCB2JV","json":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV.json","graph_json":"https://pith.science/api/pith-number/YTWJF3H5KXYRTZBDQK5VGCB2JV/graph.json","events_json":"https://pith.science/api/pith-number/YTWJF3H5KXYRTZBDQK5VGCB2JV/events.json","paper":"https://pith.science/paper/YTWJF3H5"},"agent_actions":{"view_html":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV","download_json":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV.json","view_paper":"https://pith.science/paper/YTWJF3H5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.07674&json=true","fetch_graph":"https://pith.science/api/pith-number/YTWJF3H5KXYRTZBDQK5VGCB2JV/graph.json","fetch_events":"https://pith.science/api/pith-number/YTWJF3H5KXYRTZBDQK5VGCB2JV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV/action/storage_attestation","attest_author":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV/action/author_attestation","sign_citation":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV/action/citation_signature","submit_replication":"https://pith.science/pith/YTWJF3H5KXYRTZBDQK5VGCB2JV/action/replication_record"}},"created_at":"2026-05-17T23:43:43.250428+00:00","updated_at":"2026-05-17T23:43:43.250428+00:00"}