{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7MCCXQPC3OJNG7VT5ZNEIJGZZL","short_pith_number":"pith:7MCCXQPC","schema_version":"1.0","canonical_sha256":"fb042bc1e2db92d37eb3ee5a4424d9cacc00962865eeada73d59bb75cebb0b79","source":{"kind":"arxiv","id":"1908.10999","version":3},"attestation_state":"computed","paper":{"title":"Spectral Regularization for Combating Mode Collapse in GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fei Zhou, Guoping Qiu, Kanglin Liu, Wenming Tang","submitted_at":"2019-08-29T00:56:47Z","abstract_excerpt":"Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal solution to the discriminator has a strong relationship to the spectral distributions of the weight matrix.Therefore, we monitor the spectral distribution in the discriminator of spectral normalized GANs (SN-GANs), and discover a phenomenon which we refer to as spectral collapse,"},"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":"1908.10999","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-29T00:56:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1c9ee86cc8a581349b54ce91bb2bf9bfb46a5a7e53c1dd322d8b2a00f2f8ea4b","abstract_canon_sha256":"8c7c8b4866f75cef3c3bd867b3cba660d6721664fabc537a9f02f93b023b3033"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:11:36.024688Z","signature_b64":"HaYNaEqtrx7gGS7ygsrceVcZLLQ0W9Nddrjz0fjMmAys0Ifo4Uqwc8SFenxOwOUkO3vwJGhfSw2VL283SpH4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb042bc1e2db92d37eb3ee5a4424d9cacc00962865eeada73d59bb75cebb0b79","last_reissued_at":"2026-07-05T00:11:36.024292Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:11:36.024292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral Regularization for Combating Mode Collapse in GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fei Zhou, Guoping Qiu, Kanglin Liu, Wenming Tang","submitted_at":"2019-08-29T00:56:47Z","abstract_excerpt":"Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal solution to the discriminator has a strong relationship to the spectral distributions of the weight matrix.Therefore, we monitor the spectral distribution in the discriminator of spectral normalized GANs (SN-GANs), and discover a phenomenon which we refer to as spectral collapse,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10999","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.10999/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":"1908.10999","created_at":"2026-07-05T00:11:36.024357+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.10999v3","created_at":"2026-07-05T00:11:36.024357+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10999","created_at":"2026-07-05T00:11:36.024357+00:00"},{"alias_kind":"pith_short_12","alias_value":"7MCCXQPC3OJN","created_at":"2026-07-05T00:11:36.024357+00:00"},{"alias_kind":"pith_short_16","alias_value":"7MCCXQPC3OJNG7VT","created_at":"2026-07-05T00:11:36.024357+00:00"},{"alias_kind":"pith_short_8","alias_value":"7MCCXQPC","created_at":"2026-07-05T00:11:36.024357+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/7MCCXQPC3OJNG7VT5ZNEIJGZZL","json":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL.json","graph_json":"https://pith.science/api/pith-number/7MCCXQPC3OJNG7VT5ZNEIJGZZL/graph.json","events_json":"https://pith.science/api/pith-number/7MCCXQPC3OJNG7VT5ZNEIJGZZL/events.json","paper":"https://pith.science/paper/7MCCXQPC"},"agent_actions":{"view_html":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL","download_json":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL.json","view_paper":"https://pith.science/paper/7MCCXQPC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.10999&json=true","fetch_graph":"https://pith.science/api/pith-number/7MCCXQPC3OJNG7VT5ZNEIJGZZL/graph.json","fetch_events":"https://pith.science/api/pith-number/7MCCXQPC3OJNG7VT5ZNEIJGZZL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL/action/storage_attestation","attest_author":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL/action/author_attestation","sign_citation":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL/action/citation_signature","submit_replication":"https://pith.science/pith/7MCCXQPC3OJNG7VT5ZNEIJGZZL/action/replication_record"}},"created_at":"2026-07-05T00:11:36.024357+00:00","updated_at":"2026-07-05T00:11:36.024357+00:00"}