{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WSBKQYYBCLTYUASNV3CDLTDRQW","short_pith_number":"pith:WSBKQYYB","canonical_record":{"source":{"id":"1905.00643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T09:50:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"78f17e324e36f4e2a20071c64b02e21a798302d3308ebd2021aed34eca78ded1","abstract_canon_sha256":"9f9daab3bda1658db432999ba024057a853f08eeec2739e245506c2d7e0c2737"},"schema_version":"1.0"},"canonical_sha256":"b482a8630112e78a024daec435cc7185bfdd9170c6cfd022a9f061e80f0163cc","source":{"kind":"arxiv","id":"1905.00643","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00643","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00643v1","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00643","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"pith_short_12","alias_value":"WSBKQYYBCLTY","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WSBKQYYBCLTYUASN","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WSBKQYYB","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WSBKQYYBCLTYUASNV3CDLTDRQW","target":"record","payload":{"canonical_record":{"source":{"id":"1905.00643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T09:50:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"78f17e324e36f4e2a20071c64b02e21a798302d3308ebd2021aed34eca78ded1","abstract_canon_sha256":"9f9daab3bda1658db432999ba024057a853f08eeec2739e245506c2d7e0c2737"},"schema_version":"1.0"},"canonical_sha256":"b482a8630112e78a024daec435cc7185bfdd9170c6cfd022a9f061e80f0163cc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:11.357265Z","signature_b64":"PN9VaIjpmVeUuiKWPaP/iCgmCVfHDaw0/7QBjH7URU/aIXcoYCX/1aiTxK5dNmDDL/952f/nqPfaXeFVIQfaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b482a8630112e78a024daec435cc7185bfdd9170c6cfd022a9f061e80f0163cc","last_reissued_at":"2026-05-17T23:47:11.356870Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:11.356870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.00643","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":"2026-05-17T23:47:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HRn/3O+Nq899lCj8xoESZPJnC2CoodXc9lUhWk6iWzKMo0QIzQDBSnRg2zK4Xo6sXXXIsX5vA+Kb182JrMU9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T22:12:28.945018Z"},"content_sha256":"fd9753a2b68a5248cfec93078764660fbcad3ed18e3339167b5739afbddab80b","schema_version":"1.0","event_id":"sha256:fd9753a2b68a5248cfec93078764660fbcad3ed18e3339167b5739afbddab80b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WSBKQYYBCLTYUASNV3CDLTDRQW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"James Bailey, Michael E. Houle, Sarah Monazam Erfani, Sukarna Barua, Xingjun Ma","submitted_at":"2019-05-02T09:50:27Z","abstract_excerpt":"Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of real-world data with respect to neighborhoods found in GAN-generated samples. Intuitively, CrossLID measures the d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00643","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":"2026-05-17T23:47:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+tV6uO3OuB8hvJ6+0a/ijxuLhFbQ8nD8wHyHsIqt1AmC1Ox7ylnsvCmFDEdBVdm/qnltlzH+0AsygSRUc0G6AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T22:12:28.945769Z"},"content_sha256":"a0983f8e0879277a47c3eeb9528dccb9a98575662609e29ea6f52197bd25d68b","schema_version":"1.0","event_id":"sha256:a0983f8e0879277a47c3eeb9528dccb9a98575662609e29ea6f52197bd25d68b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/bundle.json","state_url":"https://pith.science/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/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":"2026-06-09T22:12:28Z","links":{"resolver":"https://pith.science/pith/WSBKQYYBCLTYUASNV3CDLTDRQW","bundle":"https://pith.science/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/bundle.json","state":"https://pith.science/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WSBKQYYBCLTYUASNV3CDLTDRQW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WSBKQYYBCLTYUASNV3CDLTDRQW","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":"9f9daab3bda1658db432999ba024057a853f08eeec2739e245506c2d7e0c2737","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T09:50:27Z","title_canon_sha256":"78f17e324e36f4e2a20071c64b02e21a798302d3308ebd2021aed34eca78ded1"},"schema_version":"1.0","source":{"id":"1905.00643","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00643","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00643v1","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00643","created_at":"2026-05-17T23:47:11Z"},{"alias_kind":"pith_short_12","alias_value":"WSBKQYYBCLTY","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WSBKQYYBCLTYUASN","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WSBKQYYB","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:a0983f8e0879277a47c3eeb9528dccb9a98575662609e29ea6f52197bd25d68b","target":"graph","created_at":"2026-05-17T23:47:11Z","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 an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of real-world data with respect to neighborhoods found in GAN-generated samples. Intuitively, CrossLID measures the d","authors_text":"James Bailey, Michael E. Houle, Sarah Monazam Erfani, Sukarna Barua, Xingjun Ma","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T09:50:27Z","title":"Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00643","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:fd9753a2b68a5248cfec93078764660fbcad3ed18e3339167b5739afbddab80b","target":"record","created_at":"2026-05-17T23:47:11Z","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":"9f9daab3bda1658db432999ba024057a853f08eeec2739e245506c2d7e0c2737","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T09:50:27Z","title_canon_sha256":"78f17e324e36f4e2a20071c64b02e21a798302d3308ebd2021aed34eca78ded1"},"schema_version":"1.0","source":{"id":"1905.00643","kind":"arxiv","version":1}},"canonical_sha256":"b482a8630112e78a024daec435cc7185bfdd9170c6cfd022a9f061e80f0163cc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b482a8630112e78a024daec435cc7185bfdd9170c6cfd022a9f061e80f0163cc","first_computed_at":"2026-05-17T23:47:11.356870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:11.356870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PN9VaIjpmVeUuiKWPaP/iCgmCVfHDaw0/7QBjH7URU/aIXcoYCX/1aiTxK5dNmDDL/952f/nqPfaXeFVIQfaCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:11.357265Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.00643","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd9753a2b68a5248cfec93078764660fbcad3ed18e3339167b5739afbddab80b","sha256:a0983f8e0879277a47c3eeb9528dccb9a98575662609e29ea6f52197bd25d68b"],"state_sha256":"6da7dbfc84e1b7addddb28a87d15e3135991657def20719b734730c0f7982d59"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8+ep76FHYcQ7ffCsiDnXKV1qqs+1hhxW0/NOtQhCUAyGssEp7jCu8Z0Eu9OY44HvtFLat0D2xvVFSQjDr+goCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T22:12:28.949834Z","bundle_sha256":"8610ededc43f96c360787f889bbd13f195c1d14d71d2514ec93eebac5828b809"}}