{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:SBGCPKJ2WPFV3A4GDLEDT6KUWY","short_pith_number":"pith:SBGCPKJ2","canonical_record":{"source":{"id":"2009.11921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T19:34:37Z","cross_cats_sorted":["cs.IT","eess.SP","math.IT","stat.ML"],"title_canon_sha256":"5b94b7684da9296820f955a85f1922900e114a8fa4cde972b768b8c7c29ce8e4","abstract_canon_sha256":"d600638183f5f9625f6a4f388470c56ef4f922ca2a4b9d11ca0b0a7ccc49cba0"},"schema_version":"1.0"},"canonical_sha256":"904c27a93ab3cb5d83861ac839f954b625602cd2e1d4cb8156a76d4a4a99b278","source":{"kind":"arxiv","id":"2009.11921","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11921","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11921v1","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11921","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_12","alias_value":"SBGCPKJ2WPFV","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_16","alias_value":"SBGCPKJ2WPFV3A4G","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_8","alias_value":"SBGCPKJ2","created_at":"2026-07-05T01:37:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:SBGCPKJ2WPFV3A4GDLEDT6KUWY","target":"record","payload":{"canonical_record":{"source":{"id":"2009.11921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T19:34:37Z","cross_cats_sorted":["cs.IT","eess.SP","math.IT","stat.ML"],"title_canon_sha256":"5b94b7684da9296820f955a85f1922900e114a8fa4cde972b768b8c7c29ce8e4","abstract_canon_sha256":"d600638183f5f9625f6a4f388470c56ef4f922ca2a4b9d11ca0b0a7ccc49cba0"},"schema_version":"1.0"},"canonical_sha256":"904c27a93ab3cb5d83861ac839f954b625602cd2e1d4cb8156a76d4a4a99b278","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:57.807103Z","signature_b64":"3SYbqvRqiE1xG9tm+KmYy60dFjeIRTV+UXDwetO0YUT/CvmHukPSBQi5cAfzW80MQWxBy59HmRQ8gOl6nZ/9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"904c27a93ab3cb5d83861ac839f954b625602cd2e1d4cb8156a76d4a4a99b278","last_reissued_at":"2026-07-05T01:37:57.806751Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:57.806751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.11921","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":"2026-07-05T01:37:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nXyB/Ajwv+YGGLopqGchfoMrGA70d2mruMTiLI8/QYapxS1GUshzPQEiN0K2uiIYl5KchYYnimqMMtnLY8nIDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T04:16:47.308606Z"},"content_sha256":"15e04690daecce9a10c23fee35844458c9e7c754d11701eb3539be2f78404eb0","schema_version":"1.0","event_id":"sha256:15e04690daecce9a10c23fee35844458c9e7c754d11701eb3539be2f78404eb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:SBGCPKJ2WPFV3A4GDLEDT6KUWY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","eess.SP","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hossein Souri, Pirazh Khorramshahi, Rama Chellappa, Soheil Feizi","submitted_at":"2020-09-24T19:34:37Z","abstract_excerpt":"Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often formulated as a zero-sum game between two sets of functions; the generator and the discriminator. Although GANs have shown great potentials in learning complex distributions such as images, they often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. As a consequence, the diversity of generated samples is lower than that of the observed ones. To ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11921","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2009.11921/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:37:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kBk31pSuMW7NNsQLfa6SKyLsOJxDIsCZYd9G/Sxz7f48D58sFzWFiqBJHXEvuVY5DCzGptqoMC9B6ye/BqM7CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T04:16:47.309479Z"},"content_sha256":"5ccb613fd40fcdcf3d6b85ef22cf79f94b7f58142297c93cf26ad7fd5a236a4b","schema_version":"1.0","event_id":"sha256:5ccb613fd40fcdcf3d6b85ef22cf79f94b7f58142297c93cf26ad7fd5a236a4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/bundle.json","state_url":"https://pith.science/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/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":"2026-08-09T04:16:47Z","links":{"resolver":"https://pith.science/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY","bundle":"https://pith.science/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/bundle.json","state":"https://pith.science/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SBGCPKJ2WPFV3A4GDLEDT6KUWY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:SBGCPKJ2WPFV3A4GDLEDT6KUWY","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":"d600638183f5f9625f6a4f388470c56ef4f922ca2a4b9d11ca0b0a7ccc49cba0","cross_cats_sorted":["cs.IT","eess.SP","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T19:34:37Z","title_canon_sha256":"5b94b7684da9296820f955a85f1922900e114a8fa4cde972b768b8c7c29ce8e4"},"schema_version":"1.0","source":{"id":"2009.11921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11921","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11921v1","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11921","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_12","alias_value":"SBGCPKJ2WPFV","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_16","alias_value":"SBGCPKJ2WPFV3A4G","created_at":"2026-07-05T01:37:57Z"},{"alias_kind":"pith_short_8","alias_value":"SBGCPKJ2","created_at":"2026-07-05T01:37:57Z"}],"graph_snapshots":[{"event_id":"sha256:5ccb613fd40fcdcf3d6b85ef22cf79f94b7f58142297c93cf26ad7fd5a236a4b","target":"graph","created_at":"2026-07-05T01:37:57Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2009.11921/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often formulated as a zero-sum game between two sets of functions; the generator and the discriminator. Although GANs have shown great potentials in learning complex distributions such as images, they often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. As a consequence, the diversity of generated samples is lower than that of the observed ones. To ta","authors_text":"Hossein Souri, Pirazh Khorramshahi, Rama Chellappa, Soheil Feizi","cross_cats":["cs.IT","eess.SP","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T19:34:37Z","title":"GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11921","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":"sha256:15e04690daecce9a10c23fee35844458c9e7c754d11701eb3539be2f78404eb0","target":"record","created_at":"2026-07-05T01:37:57Z","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":"d600638183f5f9625f6a4f388470c56ef4f922ca2a4b9d11ca0b0a7ccc49cba0","cross_cats_sorted":["cs.IT","eess.SP","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T19:34:37Z","title_canon_sha256":"5b94b7684da9296820f955a85f1922900e114a8fa4cde972b768b8c7c29ce8e4"},"schema_version":"1.0","source":{"id":"2009.11921","kind":"arxiv","version":1}},"canonical_sha256":"904c27a93ab3cb5d83861ac839f954b625602cd2e1d4cb8156a76d4a4a99b278","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"904c27a93ab3cb5d83861ac839f954b625602cd2e1d4cb8156a76d4a4a99b278","first_computed_at":"2026-07-05T01:37:57.806751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:37:57.806751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3SYbqvRqiE1xG9tm+KmYy60dFjeIRTV+UXDwetO0YUT/CvmHukPSBQi5cAfzW80MQWxBy59HmRQ8gOl6nZ/9Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:37:57.807103Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.11921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15e04690daecce9a10c23fee35844458c9e7c754d11701eb3539be2f78404eb0","sha256:5ccb613fd40fcdcf3d6b85ef22cf79f94b7f58142297c93cf26ad7fd5a236a4b"],"state_sha256":"6cd15d30c5f445f9b693fb4e7e1637b9c2da3d8a42a97e474b3361fb1b0fc975"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NxDD5jYkhaae3hZPAAk0/xQcdrvRjOYEAR6RAosSP0PJ2eYcUITBbAusBfumQjrulY3zf491bwUCM2U6EzK4CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T04:16:47.314758Z","bundle_sha256":"28318459116cbaa059097e51ab8f61207b865b867da2b47595d4c220814ca3ff"}}