{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZRRDXYD5NP3UOGBY35FAJ4QA5L","short_pith_number":"pith:ZRRDXYD5","canonical_record":{"source":{"id":"1709.09820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T06:41:28Z","cross_cats_sorted":[],"title_canon_sha256":"60cdbd191f9ba1c59c021f83f2b6af649d009f142cfd6a6452850756937cd58f","abstract_canon_sha256":"dbd87c3818b871e1fb2a29054799e96e7c5703d59ddc7111b43c027ca3029c0a"},"schema_version":"1.0"},"canonical_sha256":"cc623be07d6bf7471838df4a04f200ead80b6e0e492013637c5ec19ebc791a42","source":{"kind":"arxiv","id":"1709.09820","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.09820","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"arxiv_version","alias_value":"1709.09820v1","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09820","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"pith_short_12","alias_value":"ZRRDXYD5NP3U","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZRRDXYD5NP3UOGBY","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZRRDXYD5","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZRRDXYD5NP3UOGBY35FAJ4QA5L","target":"record","payload":{"canonical_record":{"source":{"id":"1709.09820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T06:41:28Z","cross_cats_sorted":[],"title_canon_sha256":"60cdbd191f9ba1c59c021f83f2b6af649d009f142cfd6a6452850756937cd58f","abstract_canon_sha256":"dbd87c3818b871e1fb2a29054799e96e7c5703d59ddc7111b43c027ca3029c0a"},"schema_version":"1.0"},"canonical_sha256":"cc623be07d6bf7471838df4a04f200ead80b6e0e492013637c5ec19ebc791a42","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:06.670554Z","signature_b64":"LYs3dDkd8NX/oLAeX1aa+hQT1bQ2sMBdS7wHluJiM5dtIJBZi2XpMrNl1RTlrN6TXvxWHDHqOWGuYtdCF+ulDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc623be07d6bf7471838df4a04f200ead80b6e0e492013637c5ec19ebc791a42","last_reissued_at":"2026-05-18T00:34:06.669673Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:06.669673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.09820","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":"2026-05-18T00:34:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wEglsfcY5ZggSkb2cm3AGtcHGDYyeUDsdWqRFvKk2MpKiD1CAd8zKuPA6/p6VzbEqzUhBWR6dYmrEazJ1D0jAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:38:54.232048Z"},"content_sha256":"14856eb12a1b9726d8f55e1f6bdb78e7f6f494416895ffbfc4df9733fc6c6e38","schema_version":"1.0","event_id":"sha256:14856eb12a1b9726d8f55e1f6bdb78e7f6f494416895ffbfc4df9733fc6c6e38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZRRDXYD5NP3UOGBY35FAJ4QA5L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Adversarial Mapping Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guangxiang Zhu, Jianbo Guo, Jian Li","submitted_at":"2017-09-28T06:41:28Z","abstract_excerpt":"Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guara"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09820","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":"2026-05-18T00:34:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fvEh7VkRms8vDORdwZmeydQLMX+7qY2JArqzxFEGDurSGnt/Rql2Bl3NcLOT22ue12TC+S1mm53YdY4DQyYTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:38:54.232413Z"},"content_sha256":"3cdddb7d8c863141d502330178b5733f19757bd97809199b431b6f76e657f1ff","schema_version":"1.0","event_id":"sha256:3cdddb7d8c863141d502330178b5733f19757bd97809199b431b6f76e657f1ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/bundle.json","state_url":"https://pith.science/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/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":"2026-06-12T08:38:54Z","links":{"resolver":"https://pith.science/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L","bundle":"https://pith.science/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/bundle.json","state":"https://pith.science/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZRRDXYD5NP3UOGBY35FAJ4QA5L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZRRDXYD5NP3UOGBY35FAJ4QA5L","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":"dbd87c3818b871e1fb2a29054799e96e7c5703d59ddc7111b43c027ca3029c0a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T06:41:28Z","title_canon_sha256":"60cdbd191f9ba1c59c021f83f2b6af649d009f142cfd6a6452850756937cd58f"},"schema_version":"1.0","source":{"id":"1709.09820","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.09820","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"arxiv_version","alias_value":"1709.09820v1","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09820","created_at":"2026-05-18T00:34:06Z"},{"alias_kind":"pith_short_12","alias_value":"ZRRDXYD5NP3U","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZRRDXYD5NP3UOGBY","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZRRDXYD5","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:3cdddb7d8c863141d502330178b5733f19757bd97809199b431b6f76e657f1ff","target":"graph","created_at":"2026-05-18T00:34:06Z","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 shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guara","authors_text":"Guangxiang Zhu, Jianbo Guo, Jian Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T06:41:28Z","title":"Generative Adversarial Mapping Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09820","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":"sha256:14856eb12a1b9726d8f55e1f6bdb78e7f6f494416895ffbfc4df9733fc6c6e38","target":"record","created_at":"2026-05-18T00:34:06Z","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":"dbd87c3818b871e1fb2a29054799e96e7c5703d59ddc7111b43c027ca3029c0a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T06:41:28Z","title_canon_sha256":"60cdbd191f9ba1c59c021f83f2b6af649d009f142cfd6a6452850756937cd58f"},"schema_version":"1.0","source":{"id":"1709.09820","kind":"arxiv","version":1}},"canonical_sha256":"cc623be07d6bf7471838df4a04f200ead80b6e0e492013637c5ec19ebc791a42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc623be07d6bf7471838df4a04f200ead80b6e0e492013637c5ec19ebc791a42","first_computed_at":"2026-05-18T00:34:06.669673Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:06.669673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LYs3dDkd8NX/oLAeX1aa+hQT1bQ2sMBdS7wHluJiM5dtIJBZi2XpMrNl1RTlrN6TXvxWHDHqOWGuYtdCF+ulDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:06.670554Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.09820","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14856eb12a1b9726d8f55e1f6bdb78e7f6f494416895ffbfc4df9733fc6c6e38","sha256:3cdddb7d8c863141d502330178b5733f19757bd97809199b431b6f76e657f1ff"],"state_sha256":"69967665d9671fc3a08c4b96b7af38dfe5236acc987577e0e69135992e62e1fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UPUUmETSSAbfhBOd9EGrdtr+lkWIo9C873DE3ip/6goVKd5D6e1W1kMUrgyCQ6VdAYEiSvWZkMXh1Mnz8dVdBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T08:38:54.234357Z","bundle_sha256":"826c912ed727775ddb8ec46bdc869a6974b329ffaac9bfc08225d8252b81c164"}}