{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:H7OXMTCL6SJKMN7DUPHYXGGCPA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fc85c880c2f26a1853800dfd3f4b9baec7951a4518fc786fc7844a6f01438c71","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-10T18:49:07Z","title_canon_sha256":"1fde7839b0a7f709fdd8117e4c619b10819585d74408f3bf387d58e9dd453322"},"schema_version":"1.0","source":{"id":"1706.03269","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.03269","created_at":"2026-05-18T00:42:37Z"},{"alias_kind":"arxiv_version","alias_value":"1706.03269v1","created_at":"2026-05-18T00:42:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03269","created_at":"2026-05-18T00:42:37Z"},{"alias_kind":"pith_short_12","alias_value":"H7OXMTCL6SJK","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"H7OXMTCL6SJKMN7D","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"H7OXMTCL","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:7c5940b10dda78266dbb921b2e0ed4f07c673edfbd99d92a7ceff7d905af99a9","target":"graph","created_at":"2026-05-18T00:42:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architecture","authors_text":"Andreas Krause, Aurelien Lucchi, Kfir Y. Levy, Paulina Grnarova, Thomas Hofmann","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-10T18:49:07Z","title":"An Online Learning Approach to Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03269","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d5e3e4924f08ae5732fa237ae0ec9f78348734bd829446fe528eefde36e0684b","target":"record","created_at":"2026-05-18T00:42:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fc85c880c2f26a1853800dfd3f4b9baec7951a4518fc786fc7844a6f01438c71","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-10T18:49:07Z","title_canon_sha256":"1fde7839b0a7f709fdd8117e4c619b10819585d74408f3bf387d58e9dd453322"},"schema_version":"1.0","source":{"id":"1706.03269","kind":"arxiv","version":1}},"canonical_sha256":"3fdd764c4bf492a637e3a3cf8b98c2781fb70df0d86f5d84de1ab620b5db315e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3fdd764c4bf492a637e3a3cf8b98c2781fb70df0d86f5d84de1ab620b5db315e","first_computed_at":"2026-05-18T00:42:37.968796Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:37.968796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D0PZhw7Oz21eduLsQdEaVSMEuEjJ4cWA4lYSdlUIQmmfzU3kRfLBo7pvFx3NdphPf9hoIydQykhMu6JIhExVDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:37.969405Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.03269","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d5e3e4924f08ae5732fa237ae0ec9f78348734bd829446fe528eefde36e0684b","sha256:7c5940b10dda78266dbb921b2e0ed4f07c673edfbd99d92a7ceff7d905af99a9"],"state_sha256":"5a30e5865930d37e3de20f69d148fa2596306a7c02c12f5ffa038fcf35264172"}