{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:6QQJ6LUQU6EF63HX533QRKZCW2","short_pith_number":"pith:6QQJ6LUQ","canonical_record":{"source":{"id":"2009.11016","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-23T09:33:24Z","cross_cats_sorted":[],"title_canon_sha256":"cbc3654cfd9c04a02e330aa75c029b36b78f646a4a6ad29fd04ab188c3a0e650","abstract_canon_sha256":"8f0268d5aa9dff4bcdf3218f27a0729bc46214368c61b9980833334ea1be9670"},"schema_version":"1.0"},"canonical_sha256":"f4209f2e90a7885f6cf7eef708ab22b6b519c441635e5d6e5b5a0800f30a73eb","source":{"kind":"arxiv","id":"2009.11016","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11016","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11016v1","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11016","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_12","alias_value":"6QQJ6LUQU6EF","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_16","alias_value":"6QQJ6LUQU6EF63HX","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_8","alias_value":"6QQJ6LUQ","created_at":"2026-07-05T01:37:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:6QQJ6LUQU6EF63HX533QRKZCW2","target":"record","payload":{"canonical_record":{"source":{"id":"2009.11016","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-23T09:33:24Z","cross_cats_sorted":[],"title_canon_sha256":"cbc3654cfd9c04a02e330aa75c029b36b78f646a4a6ad29fd04ab188c3a0e650","abstract_canon_sha256":"8f0268d5aa9dff4bcdf3218f27a0729bc46214368c61b9980833334ea1be9670"},"schema_version":"1.0"},"canonical_sha256":"f4209f2e90a7885f6cf7eef708ab22b6b519c441635e5d6e5b5a0800f30a73eb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:35.616259Z","signature_b64":"q27LuBrUKLQDZD9/MmcXF4Z2YPAC3T0smuQKJKBOF0tc6cVPTcm3L4ctR+VPrVlWvbxxzsfEk0hJkAwqNrl7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4209f2e90a7885f6cf7eef708ab22b6b519c441635e5d6e5b5a0800f30a73eb","last_reissued_at":"2026-07-05T01:37:35.615884Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:35.615884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.11016","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":"2026-07-05T01:37:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"feeo5a7PLP29M97Ocd6WYzhrnFNGSfwlJA5ytZXmOa2EpTzfvk+Keg7rUdfg23t8HJ/x8BYYDIrXHSwccjLaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:36:21.522078Z"},"content_sha256":"320b807101d04db1e44e273a26b54bf045a4887e9a4c9ab60fdf465c2a96abb2","schema_version":"1.0","event_id":"sha256:320b807101d04db1e44e273a26b54bf045a4887e9a4c9ab60fdf465c2a96abb2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:6QQJ6LUQU6EF63HX533QRKZCW2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Model without Prior Distribution Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Geng, Jia Wang, Li Chen, Zhiyong Gao","submitted_at":"2020-09-23T09:33:24Z","abstract_excerpt":"Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e.g., Gaussian distribution). Their advantages over GAN are that they can simultaneously generate high dimensional data and learn latent representations to reconstruct the inputs. However, it has been observed that a trade-off exists between reconstruction and generation since matching prior distribution may destroy the geometric structure of data manifold. To mitigate this problem, we propose to let the prior match the embedding"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11016","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/2009.11016/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":"2026-07-05T01:37:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxZ/9ZSuEjwoaeRTA/mlTjHlnAOTFa8dhGnvbzv5D/7DZGUHVwTI0zhl8NuHS01Rj5u4AmQmKgvTKPuA+ym9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:36:21.522487Z"},"content_sha256":"6b604f55fcc7a25fd0c0f9c9a1ed899c669181a6aa2a8f6001f8a70f022a1d9a","schema_version":"1.0","event_id":"sha256:6b604f55fcc7a25fd0c0f9c9a1ed899c669181a6aa2a8f6001f8a70f022a1d9a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6QQJ6LUQU6EF63HX533QRKZCW2/bundle.json","state_url":"https://pith.science/pith/6QQJ6LUQU6EF63HX533QRKZCW2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6QQJ6LUQU6EF63HX533QRKZCW2/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":"2026-07-08T07:36:21Z","links":{"resolver":"https://pith.science/pith/6QQJ6LUQU6EF63HX533QRKZCW2","bundle":"https://pith.science/pith/6QQJ6LUQU6EF63HX533QRKZCW2/bundle.json","state":"https://pith.science/pith/6QQJ6LUQU6EF63HX533QRKZCW2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6QQJ6LUQU6EF63HX533QRKZCW2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6QQJ6LUQU6EF63HX533QRKZCW2","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":"8f0268d5aa9dff4bcdf3218f27a0729bc46214368c61b9980833334ea1be9670","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-23T09:33:24Z","title_canon_sha256":"cbc3654cfd9c04a02e330aa75c029b36b78f646a4a6ad29fd04ab188c3a0e650"},"schema_version":"1.0","source":{"id":"2009.11016","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11016","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11016v1","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11016","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_12","alias_value":"6QQJ6LUQU6EF","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_16","alias_value":"6QQJ6LUQU6EF63HX","created_at":"2026-07-05T01:37:35Z"},{"alias_kind":"pith_short_8","alias_value":"6QQJ6LUQ","created_at":"2026-07-05T01:37:35Z"}],"graph_snapshots":[{"event_id":"sha256:6b604f55fcc7a25fd0c0f9c9a1ed899c669181a6aa2a8f6001f8a70f022a1d9a","target":"graph","created_at":"2026-07-05T01:37:35Z","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/2009.11016/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e.g., Gaussian distribution). Their advantages over GAN are that they can simultaneously generate high dimensional data and learn latent representations to reconstruct the inputs. However, it has been observed that a trade-off exists between reconstruction and generation since matching prior distribution may destroy the geometric structure of data manifold. To mitigate this problem, we propose to let the prior match the embedding","authors_text":"Cong Geng, Jia Wang, Li Chen, Zhiyong Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-23T09:33:24Z","title":"Generative Model without Prior Distribution Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11016","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":"sha256:320b807101d04db1e44e273a26b54bf045a4887e9a4c9ab60fdf465c2a96abb2","target":"record","created_at":"2026-07-05T01:37:35Z","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":"8f0268d5aa9dff4bcdf3218f27a0729bc46214368c61b9980833334ea1be9670","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-23T09:33:24Z","title_canon_sha256":"cbc3654cfd9c04a02e330aa75c029b36b78f646a4a6ad29fd04ab188c3a0e650"},"schema_version":"1.0","source":{"id":"2009.11016","kind":"arxiv","version":1}},"canonical_sha256":"f4209f2e90a7885f6cf7eef708ab22b6b519c441635e5d6e5b5a0800f30a73eb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4209f2e90a7885f6cf7eef708ab22b6b519c441635e5d6e5b5a0800f30a73eb","first_computed_at":"2026-07-05T01:37:35.615884Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:37:35.615884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q27LuBrUKLQDZD9/MmcXF4Z2YPAC3T0smuQKJKBOF0tc6cVPTcm3L4ctR+VPrVlWvbxxzsfEk0hJkAwqNrl7Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:37:35.616259Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.11016","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:320b807101d04db1e44e273a26b54bf045a4887e9a4c9ab60fdf465c2a96abb2","sha256:6b604f55fcc7a25fd0c0f9c9a1ed899c669181a6aa2a8f6001f8a70f022a1d9a"],"state_sha256":"93f2ff62caff2bb79982529f1fee6f45f0c82e338c3e8d2176a254dfac691458"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sdLLCDwBYMPx6N6clxFXHTKVLfayKW39V4P6/89CpMIry7TfzGHEcSVAFA87Y3VdyC60aX/TzcjAcbj7B/amAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T07:36:21.524856Z","bundle_sha256":"4ff0283d8fcb06a1ebc488b6aa0dc5387102ece0a07952a12f0c996cbdb53591"}}