{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6GVRAZMGDF3Z52HGPXNXS3YBPN","short_pith_number":"pith:6GVRAZMG","canonical_record":{"source":{"id":"1707.07530","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-24T12:58:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab53051d91f35179deef25cb4fcf5ad4d4e79ac73a883ce333c72b695e9e2b4d","abstract_canon_sha256":"1631faa8ed3f4ebb41da794842a06ad7f6f0274cd9756d4e741884fd1466fec1"},"schema_version":"1.0"},"canonical_sha256":"f1ab10658619779ee8e67ddb796f017b440936af17a217aa5f75ff812cdd95b1","source":{"kind":"arxiv","id":"1707.07530","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07530","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07530v1","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07530","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"pith_short_12","alias_value":"6GVRAZMGDF3Z","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6GVRAZMGDF3Z52HG","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6GVRAZMG","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6GVRAZMGDF3Z52HGPXNXS3YBPN","target":"record","payload":{"canonical_record":{"source":{"id":"1707.07530","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-24T12:58:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab53051d91f35179deef25cb4fcf5ad4d4e79ac73a883ce333c72b695e9e2b4d","abstract_canon_sha256":"1631faa8ed3f4ebb41da794842a06ad7f6f0274cd9756d4e741884fd1466fec1"},"schema_version":"1.0"},"canonical_sha256":"f1ab10658619779ee8e67ddb796f017b440936af17a217aa5f75ff812cdd95b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:50.573029Z","signature_b64":"T4skKCM/ekC3fkAGWXBbwB34gSxUOmjYijXo1Ifvq9FurHyKc2WFDhj9eWyOXDZs5LenB9BKREPW6eOHmMpMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1ab10658619779ee8e67ddb796f017b440936af17a217aa5f75ff812cdd95b1","last_reissued_at":"2026-05-18T00:30:50.572466Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:50.572466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.07530","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":"2026-05-18T00:30:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dIUF+3QAnBdmZTDWugBB6J2VhOPfZOS2Dk9lJAEyqy/fIlm1SaGkdh8y/YkaXCtG4welrtpteNaSX7TzeIUdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T20:15:19.264680Z"},"content_sha256":"de13dee7d6ea61d84a4e25a5d163812143321acb600958506cab92371e3c1d31","schema_version":"1.0","event_id":"sha256:de13dee7d6ea61d84a4e25a5d163812143321acb600958506cab92371e3c1d31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6GVRAZMGDF3Z52HGPXNXS3YBPN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Likelihood Estimation for Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Gerhard Widmer, Hamid Eghbal-zadeh","submitted_at":"2017-07-24T12:58:46Z","abstract_excerpt":"We present a simple method for assessing the quality of generated images in Generative Adversarial Networks (GANs). The method can be applied in any kind of GAN without interfering with the learning procedure or affecting the learning objective. The central idea is to define a likelihood function that correlates with the quality of the generated images. In particular, we derive a Gaussian likelihood function from the distribution of the embeddings (hidden activations) of the real images in the discriminator, and based on this, define two simple measures of how likely it is that the embeddings "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07530","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":"2026-05-18T00:30:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JAtiPM6uszhtmudQHCo9+JEAl815r2bZiQgRb9j1upeAEnG/F9xyc6UFL/pjF0duBVG0ZDS/0dJrxhdOGiyrCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T20:15:19.265076Z"},"content_sha256":"8c7cbbb37834bb81a0bd0df28fa98d19ba7247978fa9f500f92d1d2736fa78e0","schema_version":"1.0","event_id":"sha256:8c7cbbb37834bb81a0bd0df28fa98d19ba7247978fa9f500f92d1d2736fa78e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/bundle.json","state_url":"https://pith.science/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/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":"2026-05-23T20:15:19Z","links":{"resolver":"https://pith.science/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN","bundle":"https://pith.science/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/bundle.json","state":"https://pith.science/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6GVRAZMGDF3Z52HGPXNXS3YBPN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6GVRAZMGDF3Z52HGPXNXS3YBPN","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":"1631faa8ed3f4ebb41da794842a06ad7f6f0274cd9756d4e741884fd1466fec1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-24T12:58:46Z","title_canon_sha256":"ab53051d91f35179deef25cb4fcf5ad4d4e79ac73a883ce333c72b695e9e2b4d"},"schema_version":"1.0","source":{"id":"1707.07530","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07530","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07530v1","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07530","created_at":"2026-05-18T00:30:50Z"},{"alias_kind":"pith_short_12","alias_value":"6GVRAZMGDF3Z","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6GVRAZMGDF3Z52HG","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6GVRAZMG","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:8c7cbbb37834bb81a0bd0df28fa98d19ba7247978fa9f500f92d1d2736fa78e0","target":"graph","created_at":"2026-05-18T00:30:50Z","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":"We present a simple method for assessing the quality of generated images in Generative Adversarial Networks (GANs). The method can be applied in any kind of GAN without interfering with the learning procedure or affecting the learning objective. The central idea is to define a likelihood function that correlates with the quality of the generated images. In particular, we derive a Gaussian likelihood function from the distribution of the embeddings (hidden activations) of the real images in the discriminator, and based on this, define two simple measures of how likely it is that the embeddings ","authors_text":"Gerhard Widmer, Hamid Eghbal-zadeh","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-24T12:58:46Z","title":"Likelihood Estimation for Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07530","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:de13dee7d6ea61d84a4e25a5d163812143321acb600958506cab92371e3c1d31","target":"record","created_at":"2026-05-18T00:30:50Z","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":"1631faa8ed3f4ebb41da794842a06ad7f6f0274cd9756d4e741884fd1466fec1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-24T12:58:46Z","title_canon_sha256":"ab53051d91f35179deef25cb4fcf5ad4d4e79ac73a883ce333c72b695e9e2b4d"},"schema_version":"1.0","source":{"id":"1707.07530","kind":"arxiv","version":1}},"canonical_sha256":"f1ab10658619779ee8e67ddb796f017b440936af17a217aa5f75ff812cdd95b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1ab10658619779ee8e67ddb796f017b440936af17a217aa5f75ff812cdd95b1","first_computed_at":"2026-05-18T00:30:50.572466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:50.572466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T4skKCM/ekC3fkAGWXBbwB34gSxUOmjYijXo1Ifvq9FurHyKc2WFDhj9eWyOXDZs5LenB9BKREPW6eOHmMpMBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:50.573029Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.07530","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de13dee7d6ea61d84a4e25a5d163812143321acb600958506cab92371e3c1d31","sha256:8c7cbbb37834bb81a0bd0df28fa98d19ba7247978fa9f500f92d1d2736fa78e0"],"state_sha256":"d510c3528193924f8a98f48b679b2e3c43b8d47b80e07147254f32b7f5308ec4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6C6Qv8M0LAxJ7a49vf5ScHH5ARJvWv+8K4IU0K87UccT1dA2yrS1Q53Uo0SJVX+O2NG61Tvj9uQ2z9A5Nf7ABw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T20:15:19.268376Z","bundle_sha256":"318c2f49e1174acc6844808283a2a8ee1c3f73be6b2f1690ac8a1be9688dea88"}}