{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TOC24X77EXHNW5VZ23FUMMX4ND","short_pith_number":"pith:TOC24X77","canonical_record":{"source":{"id":"1705.09367","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-25T21:17:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"15f7d5bf04e864a2c5deb5521cc137893e1c0a234ec8e099d3260e66ed937150","abstract_canon_sha256":"8e51de41494f5d1ee704c017f4b65b8641087a81d553682dbbb03b788753a238"},"schema_version":"1.0"},"canonical_sha256":"9b85ae5fff25cedb76b9d6cb4632fc68e35477fdc489b78aa922e6dacb84c386","source":{"kind":"arxiv","id":"1705.09367","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.09367","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.09367v2","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09367","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"TOC24X77EXHN","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TOC24X77EXHNW5VZ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TOC24X77","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TOC24X77EXHNW5VZ23FUMMX4ND","target":"record","payload":{"canonical_record":{"source":{"id":"1705.09367","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-25T21:17:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"15f7d5bf04e864a2c5deb5521cc137893e1c0a234ec8e099d3260e66ed937150","abstract_canon_sha256":"8e51de41494f5d1ee704c017f4b65b8641087a81d553682dbbb03b788753a238"},"schema_version":"1.0"},"canonical_sha256":"9b85ae5fff25cedb76b9d6cb4632fc68e35477fdc489b78aa922e6dacb84c386","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:12.674343Z","signature_b64":"dRhK8d0AoNfYjtX0rizW68v5LR8gLeFguTMWjRpROgv++yFq2uW/WbJea/Nkwo0aawggyOZCIeI1WH+jxoP+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b85ae5fff25cedb76b9d6cb4632fc68e35477fdc489b78aa922e6dacb84c386","last_reissued_at":"2026-05-18T00:31:12.673597Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:12.673597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.09367","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HhhuHul2JTlt7RKIOtMhYk7iEVdziypvqmGWufCW01CzvVOYZsgbGYWS9Kartn8ORipHbvTuWMl9x0Y3vnM2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T15:09:45.407533Z"},"content_sha256":"315a78088a52bb49d28b53342d06e09add953bdefef0609d0bf8ec133b795616","schema_version":"1.0","event_id":"sha256:315a78088a52bb49d28b53342d06e09add953bdefef0609d0bf8ec133b795616"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TOC24X77EXHNW5VZ23FUMMX4ND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stabilizing Training of Generative Adversarial Networks through Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aurelien Lucchi, Kevin Roth, Sebastian Nowozin, Thomas Hofmann","submitted_at":"2017-05-25T21:17:50Z","abstract_excerpt":"Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters. This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f-divergence to be undefined. We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09367","kind":"arxiv","version":2},"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":"2026-05-18T00:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NfD/h0d3YKNJ5HP/g1kMreTB4nHCsgvXekVBviWU+x6z5WmoUSU7WJdwmxb7aa4E2PZQpH65bSfkqwwE2t4hCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T15:09:45.408252Z"},"content_sha256":"8e23db8631c3e3fcc2cf59f6d02db68534c1bf8b10f5657e3e432da115df1ca2","schema_version":"1.0","event_id":"sha256:8e23db8631c3e3fcc2cf59f6d02db68534c1bf8b10f5657e3e432da115df1ca2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TOC24X77EXHNW5VZ23FUMMX4ND/bundle.json","state_url":"https://pith.science/pith/TOC24X77EXHNW5VZ23FUMMX4ND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TOC24X77EXHNW5VZ23FUMMX4ND/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":"2026-05-23T15:09:45Z","links":{"resolver":"https://pith.science/pith/TOC24X77EXHNW5VZ23FUMMX4ND","bundle":"https://pith.science/pith/TOC24X77EXHNW5VZ23FUMMX4ND/bundle.json","state":"https://pith.science/pith/TOC24X77EXHNW5VZ23FUMMX4ND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TOC24X77EXHNW5VZ23FUMMX4ND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TOC24X77EXHNW5VZ23FUMMX4ND","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":"8e51de41494f5d1ee704c017f4b65b8641087a81d553682dbbb03b788753a238","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-25T21:17:50Z","title_canon_sha256":"15f7d5bf04e864a2c5deb5521cc137893e1c0a234ec8e099d3260e66ed937150"},"schema_version":"1.0","source":{"id":"1705.09367","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.09367","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.09367v2","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09367","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"TOC24X77EXHN","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TOC24X77EXHNW5VZ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TOC24X77","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:8e23db8631c3e3fcc2cf59f6d02db68534c1bf8b10f5657e3e432da115df1ca2","target":"graph","created_at":"2026-05-18T00:31:12Z","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":"Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters. This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f-divergence to be undefined. We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN tra","authors_text":"Aurelien Lucchi, Kevin Roth, Sebastian Nowozin, Thomas Hofmann","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-25T21:17:50Z","title":"Stabilizing Training of Generative Adversarial Networks through Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09367","kind":"arxiv","version":2},"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":"sha256:315a78088a52bb49d28b53342d06e09add953bdefef0609d0bf8ec133b795616","target":"record","created_at":"2026-05-18T00:31:12Z","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":"8e51de41494f5d1ee704c017f4b65b8641087a81d553682dbbb03b788753a238","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-25T21:17:50Z","title_canon_sha256":"15f7d5bf04e864a2c5deb5521cc137893e1c0a234ec8e099d3260e66ed937150"},"schema_version":"1.0","source":{"id":"1705.09367","kind":"arxiv","version":2}},"canonical_sha256":"9b85ae5fff25cedb76b9d6cb4632fc68e35477fdc489b78aa922e6dacb84c386","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b85ae5fff25cedb76b9d6cb4632fc68e35477fdc489b78aa922e6dacb84c386","first_computed_at":"2026-05-18T00:31:12.673597Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:12.673597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dRhK8d0AoNfYjtX0rizW68v5LR8gLeFguTMWjRpROgv++yFq2uW/WbJea/Nkwo0aawggyOZCIeI1WH+jxoP+CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:12.674343Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.09367","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:315a78088a52bb49d28b53342d06e09add953bdefef0609d0bf8ec133b795616","sha256:8e23db8631c3e3fcc2cf59f6d02db68534c1bf8b10f5657e3e432da115df1ca2"],"state_sha256":"04574cf8f7ebee2b2c9c690f1a49f257996f5f6e4ea27b58307b5dc6fb823b29"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LpGAoYZrJeAHCixASKKxlTCRtYv9gJAn33NtRH5afuR3TBSadgfl9/rrsIZvXO+3H1eRCKvM7V3d0w0cVM3eAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T15:09:45.412369Z","bundle_sha256":"baf2028d974618bcae8005b77441ade1a97aed9f3e3928c987b444c65e1d1871"}}