{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7NHJSY3N26F73SFMS7KRCOC45C","short_pith_number":"pith:7NHJSY3N","canonical_record":{"source":{"id":"1803.01045","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-02T21:18:36Z","cross_cats_sorted":[],"title_canon_sha256":"ae972900847dfca1da60f8b0584da4fa06e070dd87da904306c96a33c484cf16","abstract_canon_sha256":"da65501b6215577922172d5e0caf4284bdcb47659096f726d3851a60e7ed7151"},"schema_version":"1.0"},"canonical_sha256":"fb4e99636dd78bfdc8ac97d511385ce8981b2e1085e83ac0e7d663e2a2b9b07f","source":{"kind":"arxiv","id":"1803.01045","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01045","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01045v2","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01045","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"pith_short_12","alias_value":"7NHJSY3N26F7","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7NHJSY3N26F73SFM","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7NHJSY3N","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7NHJSY3N26F73SFMS7KRCOC45C","target":"record","payload":{"canonical_record":{"source":{"id":"1803.01045","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-02T21:18:36Z","cross_cats_sorted":[],"title_canon_sha256":"ae972900847dfca1da60f8b0584da4fa06e070dd87da904306c96a33c484cf16","abstract_canon_sha256":"da65501b6215577922172d5e0caf4284bdcb47659096f726d3851a60e7ed7151"},"schema_version":"1.0"},"canonical_sha256":"fb4e99636dd78bfdc8ac97d511385ce8981b2e1085e83ac0e7d663e2a2b9b07f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:18.535268Z","signature_b64":"2x5WgTK6UuelZq8TjshpqwAq4Jhser+pfdr9+uR5MFvnW7AbYYYNRZMg2OpeqYa65Ha/9/csDGfYPtigpTy4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb4e99636dd78bfdc8ac97d511385ce8981b2e1085e83ac0e7d663e2a2b9b07f","last_reissued_at":"2026-05-18T00:17:18.534674Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:18.534674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.01045","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":"2026-05-18T00:17:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eZJ4sdenHFn/cUBnhiwQApBIYxPJfyGmoWHJVLB3gOvboZuwZwO6GSchEm/wILQUySUYa6kPenHJfTB9G1MlBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:55:31.312779Z"},"content_sha256":"a607cc6e7a86b13597bafd81e549d36af9d0082019ead10570c62c0dcb988ea2","schema_version":"1.0","event_id":"sha256:a607cc6e7a86b13597bafd81e549d36af9d0082019ead10570c62c0dcb988ea2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7NHJSY3N26F73SFMS7KRCOC45C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantitatively Evaluating GANs With Divergences Proposed for Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel Jiwoong Im, Graham Taylor, He Ma, Kristin Branson","submitted_at":"2018-03-02T21:18:36Z","abstract_excerpt":"Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants are being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01045","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":"2026-05-18T00:17:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iU1qFvKY1czS+qWfQpkXG5IsEyb7Q+BojJdnAdvshk0x9zg+Ew+vhu8INFxrt+Z4c464nGHkqWBj0WAqToaTCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:55:31.313429Z"},"content_sha256":"546a54b8c960eebcd6268c43fdd379dd09f377fd37aa2f2b9235cf54e58b8f39","schema_version":"1.0","event_id":"sha256:546a54b8c960eebcd6268c43fdd379dd09f377fd37aa2f2b9235cf54e58b8f39"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7NHJSY3N26F73SFMS7KRCOC45C/bundle.json","state_url":"https://pith.science/pith/7NHJSY3N26F73SFMS7KRCOC45C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7NHJSY3N26F73SFMS7KRCOC45C/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":"2026-05-25T21:55:31Z","links":{"resolver":"https://pith.science/pith/7NHJSY3N26F73SFMS7KRCOC45C","bundle":"https://pith.science/pith/7NHJSY3N26F73SFMS7KRCOC45C/bundle.json","state":"https://pith.science/pith/7NHJSY3N26F73SFMS7KRCOC45C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7NHJSY3N26F73SFMS7KRCOC45C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7NHJSY3N26F73SFMS7KRCOC45C","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":"da65501b6215577922172d5e0caf4284bdcb47659096f726d3851a60e7ed7151","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-02T21:18:36Z","title_canon_sha256":"ae972900847dfca1da60f8b0584da4fa06e070dd87da904306c96a33c484cf16"},"schema_version":"1.0","source":{"id":"1803.01045","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01045","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01045v2","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01045","created_at":"2026-05-18T00:17:18Z"},{"alias_kind":"pith_short_12","alias_value":"7NHJSY3N26F7","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7NHJSY3N26F73SFM","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7NHJSY3N","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:546a54b8c960eebcd6268c43fdd379dd09f377fd37aa2f2b9235cf54e58b8f39","target":"graph","created_at":"2026-05-18T00:17:18Z","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) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants are being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used o","authors_text":"Daniel Jiwoong Im, Graham Taylor, He Ma, Kristin Branson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-02T21:18:36Z","title":"Quantitatively Evaluating GANs With Divergences Proposed for Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01045","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:a607cc6e7a86b13597bafd81e549d36af9d0082019ead10570c62c0dcb988ea2","target":"record","created_at":"2026-05-18T00:17:18Z","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":"da65501b6215577922172d5e0caf4284bdcb47659096f726d3851a60e7ed7151","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-02T21:18:36Z","title_canon_sha256":"ae972900847dfca1da60f8b0584da4fa06e070dd87da904306c96a33c484cf16"},"schema_version":"1.0","source":{"id":"1803.01045","kind":"arxiv","version":2}},"canonical_sha256":"fb4e99636dd78bfdc8ac97d511385ce8981b2e1085e83ac0e7d663e2a2b9b07f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb4e99636dd78bfdc8ac97d511385ce8981b2e1085e83ac0e7d663e2a2b9b07f","first_computed_at":"2026-05-18T00:17:18.534674Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:17:18.534674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2x5WgTK6UuelZq8TjshpqwAq4Jhser+pfdr9+uR5MFvnW7AbYYYNRZMg2OpeqYa65Ha/9/csDGfYPtigpTy4BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:17:18.535268Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.01045","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a607cc6e7a86b13597bafd81e549d36af9d0082019ead10570c62c0dcb988ea2","sha256:546a54b8c960eebcd6268c43fdd379dd09f377fd37aa2f2b9235cf54e58b8f39"],"state_sha256":"e9f749599ced385ee93c1bdf31a34fcfd6e49b3c00bf392d63db7937cc6f807f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7nI+kU1EKnZrrl0h4mbvi10bWX9m5he6a6lanvX4RQTE8GwbiZT2Q/6/aoHQwMYh8F75OP1OOsPFzixtUcFuAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:55:31.317878Z","bundle_sha256":"4a35999a6f0bc0fee3c3b25d41782088817dc6c7ecc141f3c13ce33f809326ca"}}