{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GXEP2XOV6EIWLEQWCVJ2DNSHAE","short_pith_number":"pith:GXEP2XOV","canonical_record":{"source":{"id":"1902.03984","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T16:44:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c8adb4bec062eb10390592d4367fba994afb90125c886043df2b99d30f4fa7d1","abstract_canon_sha256":"f5a1c24353a96b4369619e246c1946b8919a18304d7d61614cafba752a79c5cc"},"schema_version":"1.0"},"canonical_sha256":"35c8fd5dd5f1116592161553a1b647011ee43807a9ef092ec1cccda6706267ec","source":{"kind":"arxiv","id":"1902.03984","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03984","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03984v1","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03984","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"pith_short_12","alias_value":"GXEP2XOV6EIW","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GXEP2XOV6EIWLEQW","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GXEP2XOV","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GXEP2XOV6EIWLEQWCVJ2DNSHAE","target":"record","payload":{"canonical_record":{"source":{"id":"1902.03984","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T16:44:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c8adb4bec062eb10390592d4367fba994afb90125c886043df2b99d30f4fa7d1","abstract_canon_sha256":"f5a1c24353a96b4369619e246c1946b8919a18304d7d61614cafba752a79c5cc"},"schema_version":"1.0"},"canonical_sha256":"35c8fd5dd5f1116592161553a1b647011ee43807a9ef092ec1cccda6706267ec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:17.439849Z","signature_b64":"/ZEq0xIL+qi5lFDDtKuiRPqjVSp/4iBqTMy3okbOKE/NEFt7C6NGH0FzOfH5helbV8BDRlWJ0f6tmh0EUK9FAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35c8fd5dd5f1116592161553a1b647011ee43807a9ef092ec1cccda6706267ec","last_reissued_at":"2026-05-17T23:54:17.438483Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:17.438483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.03984","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5vstawHUfPj6vJ+BiF4XnVcyTmAlPHo33lDRdwJl1VViAjPWGm4O71tdo7YDFggGa59EfhWV+EM1/DY8G5SeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:21:52.368515Z"},"content_sha256":"b28205dabce28c73d57156dabcce1cbbb179e097bff496a93664b854441e1f81","schema_version":"1.0","event_id":"sha256:b28205dabce28c73d57156dabcce1cbbb179e097bff496a93664b854441e1f81"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GXEP2XOV6EIWLEQWCVJ2DNSHAE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Generalization and Stability of Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hoang Thanh-Tung, Svetha Venkatesh, Truyen Tran","submitted_at":"2019-02-11T16:44:16Z","abstract_excerpt":"Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discrimin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03984","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9LwIXH9cJS5yCckf8Okxe0wpiRDZzGfja79Ud9+TCgICuQDt+/0fR7fw30N4xOfAypCxYV4ESDNMFCMzEknJBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:21:52.369190Z"},"content_sha256":"7dfb94128d7a5fd8be36cb240c34cf59ff003586489059d4216c751191317c7d","schema_version":"1.0","event_id":"sha256:7dfb94128d7a5fd8be36cb240c34cf59ff003586489059d4216c751191317c7d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/bundle.json","state_url":"https://pith.science/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-29T21:21:52Z","links":{"resolver":"https://pith.science/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE","bundle":"https://pith.science/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/bundle.json","state":"https://pith.science/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GXEP2XOV6EIWLEQWCVJ2DNSHAE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GXEP2XOV6EIWLEQWCVJ2DNSHAE","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":"f5a1c24353a96b4369619e246c1946b8919a18304d7d61614cafba752a79c5cc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T16:44:16Z","title_canon_sha256":"c8adb4bec062eb10390592d4367fba994afb90125c886043df2b99d30f4fa7d1"},"schema_version":"1.0","source":{"id":"1902.03984","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03984","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03984v1","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03984","created_at":"2026-05-17T23:54:17Z"},{"alias_kind":"pith_short_12","alias_value":"GXEP2XOV6EIW","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GXEP2XOV6EIWLEQW","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GXEP2XOV","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:7dfb94128d7a5fd8be36cb240c34cf59ff003586489059d4216c751191317c7d","target":"graph","created_at":"2026-05-17T23:54:17Z","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":"Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discrimin","authors_text":"Hoang Thanh-Tung, Svetha Venkatesh, Truyen Tran","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T16:44:16Z","title":"Improving Generalization and Stability of Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03984","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:b28205dabce28c73d57156dabcce1cbbb179e097bff496a93664b854441e1f81","target":"record","created_at":"2026-05-17T23:54:17Z","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":"f5a1c24353a96b4369619e246c1946b8919a18304d7d61614cafba752a79c5cc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T16:44:16Z","title_canon_sha256":"c8adb4bec062eb10390592d4367fba994afb90125c886043df2b99d30f4fa7d1"},"schema_version":"1.0","source":{"id":"1902.03984","kind":"arxiv","version":1}},"canonical_sha256":"35c8fd5dd5f1116592161553a1b647011ee43807a9ef092ec1cccda6706267ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35c8fd5dd5f1116592161553a1b647011ee43807a9ef092ec1cccda6706267ec","first_computed_at":"2026-05-17T23:54:17.438483Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:17.438483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/ZEq0xIL+qi5lFDDtKuiRPqjVSp/4iBqTMy3okbOKE/NEFt7C6NGH0FzOfH5helbV8BDRlWJ0f6tmh0EUK9FAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:17.439849Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.03984","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b28205dabce28c73d57156dabcce1cbbb179e097bff496a93664b854441e1f81","sha256:7dfb94128d7a5fd8be36cb240c34cf59ff003586489059d4216c751191317c7d"],"state_sha256":"b392f739cf935c31e715fc7927c0c8280dff1fbb5cd445d4a7efd02ad80d1d08"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WkXSnhWwRjJtQ8at3JQoPWOewhFaClYt6vKrU+nKM3CodHrwGcKpAOweQbO3qEpI2MW8nbh91EhrBoEahl5gCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T21:21:52.373360Z","bundle_sha256":"7605895a4f666092c912465abd22dc7fa688293fe670756f2063bd2ef046ab9d"}}