{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:6ZBNMFLJOXIS7EDZPMSSAZKTR7","short_pith_number":"pith:6ZBNMFLJ","canonical_record":{"source":{"id":"1609.09408","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-29T16:14:45Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"cd8db2f1aed642a9b28b420e4848cad8f480ab73e61d656a25d2711ad2b0c354","abstract_canon_sha256":"b389f46d9022822076e7dce99a9e64390b41734ba6545584bf9111ba3df15e7f"},"schema_version":"1.0"},"canonical_sha256":"f642d6156975d12f90797b252065538fe0d4ffb9bd302abe775cf6a2a2fc54e9","source":{"kind":"arxiv","id":"1609.09408","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.09408","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"arxiv_version","alias_value":"1609.09408v3","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09408","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"pith_short_12","alias_value":"6ZBNMFLJOXIS","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"6ZBNMFLJOXIS7EDZ","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"6ZBNMFLJ","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:6ZBNMFLJOXIS7EDZPMSSAZKTR7","target":"record","payload":{"canonical_record":{"source":{"id":"1609.09408","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-29T16:14:45Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"cd8db2f1aed642a9b28b420e4848cad8f480ab73e61d656a25d2711ad2b0c354","abstract_canon_sha256":"b389f46d9022822076e7dce99a9e64390b41734ba6545584bf9111ba3df15e7f"},"schema_version":"1.0"},"canonical_sha256":"f642d6156975d12f90797b252065538fe0d4ffb9bd302abe775cf6a2a2fc54e9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:03.610811Z","signature_b64":"FToUdqwRHNbH4CPe1kZgKe/E86ePqFj4Mb7aHHhTkGoVAtMOrvKcWgNaFoQhyKIBEa0F7wxvSM+FRRtn57qNDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f642d6156975d12f90797b252065538fe0d4ffb9bd302abe775cf6a2a2fc54e9","last_reissued_at":"2026-05-18T00:02:03.610199Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:03.610199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.09408","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q3XpnMkIoTQo4Nedf6WXBVm2x/43l6h4keQS5HowdOzfR+YSFlG1bQk9vDo5YkrQd4EGA2KgrvagnbVExdhPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:39:53.292341Z"},"content_sha256":"f5ff5d1be7d8baae59f3baed8ff9d78fbaee70594f4ae4349d9b2e19f7fcabf7","schema_version":"1.0","event_id":"sha256:f5ff5d1be7d8baae59f3baed8ff9d78fbaee70594f4ae4349d9b2e19f7fcabf7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:6ZBNMFLJOXIS7EDZPMSSAZKTR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cooperative Training of Descriptor and Generator Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Jianwen Xie, Ruiqi Gao, Song-Chun Zhu, Yang Lu, Ying Nian Wu","submitted_at":"2016-09-29T16:14:45Z","abstract_excerpt":"This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the energy. We call it the descriptor network. The second model is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed image. The maximum likelihood learning algorithms of both models i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09408","kind":"arxiv","version":3},"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":"2026-05-18T00:02:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CiEXnGi62AexMbb/8eWVIWwNQEUQmaxEXyWn7zaYrXqwLdtLgEmO7h47BwWnqKYy0tWkONWWJuG/hAEXoIwnBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:39:53.292948Z"},"content_sha256":"8b9f4e6700dd1d00d832d7e710cc3b750d33e1073b68f8e5c9d21b2d1e1a1b7e","schema_version":"1.0","event_id":"sha256:8b9f4e6700dd1d00d832d7e710cc3b750d33e1073b68f8e5c9d21b2d1e1a1b7e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/bundle.json","state_url":"https://pith.science/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/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":"2026-05-30T15:39:53Z","links":{"resolver":"https://pith.science/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7","bundle":"https://pith.science/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/bundle.json","state":"https://pith.science/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6ZBNMFLJOXIS7EDZPMSSAZKTR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:6ZBNMFLJOXIS7EDZPMSSAZKTR7","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":"b389f46d9022822076e7dce99a9e64390b41734ba6545584bf9111ba3df15e7f","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-29T16:14:45Z","title_canon_sha256":"cd8db2f1aed642a9b28b420e4848cad8f480ab73e61d656a25d2711ad2b0c354"},"schema_version":"1.0","source":{"id":"1609.09408","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.09408","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"arxiv_version","alias_value":"1609.09408v3","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09408","created_at":"2026-05-18T00:02:03Z"},{"alias_kind":"pith_short_12","alias_value":"6ZBNMFLJOXIS","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"6ZBNMFLJOXIS7EDZ","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"6ZBNMFLJ","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:8b9f4e6700dd1d00d832d7e710cc3b750d33e1073b68f8e5c9d21b2d1e1a1b7e","target":"graph","created_at":"2026-05-18T00:02:03Z","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":"This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the energy. We call it the descriptor network. The second model is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed image. The maximum likelihood learning algorithms of both models i","authors_text":"Jianwen Xie, Ruiqi Gao, Song-Chun Zhu, Yang Lu, Ying Nian Wu","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-29T16:14:45Z","title":"Cooperative Training of Descriptor and Generator Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09408","kind":"arxiv","version":3},"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:f5ff5d1be7d8baae59f3baed8ff9d78fbaee70594f4ae4349d9b2e19f7fcabf7","target":"record","created_at":"2026-05-18T00:02:03Z","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":"b389f46d9022822076e7dce99a9e64390b41734ba6545584bf9111ba3df15e7f","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-29T16:14:45Z","title_canon_sha256":"cd8db2f1aed642a9b28b420e4848cad8f480ab73e61d656a25d2711ad2b0c354"},"schema_version":"1.0","source":{"id":"1609.09408","kind":"arxiv","version":3}},"canonical_sha256":"f642d6156975d12f90797b252065538fe0d4ffb9bd302abe775cf6a2a2fc54e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f642d6156975d12f90797b252065538fe0d4ffb9bd302abe775cf6a2a2fc54e9","first_computed_at":"2026-05-18T00:02:03.610199Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:03.610199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FToUdqwRHNbH4CPe1kZgKe/E86ePqFj4Mb7aHHhTkGoVAtMOrvKcWgNaFoQhyKIBEa0F7wxvSM+FRRtn57qNDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:03.610811Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.09408","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f5ff5d1be7d8baae59f3baed8ff9d78fbaee70594f4ae4349d9b2e19f7fcabf7","sha256:8b9f4e6700dd1d00d832d7e710cc3b750d33e1073b68f8e5c9d21b2d1e1a1b7e"],"state_sha256":"224a3e1755a4ebf7dc2541b32b348292ec2feddf77a44173fcc2c712c7f61b2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xpGScO4ryzjPWYlMW14yoc0AgykCPbEpBdVoTqPk7DFMqOzRcPcvq6IUHxlH2PPdMGvPUzPuNZAC1CaUpUrjBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T15:39:53.296271Z","bundle_sha256":"443095f5ecc74ed67dd81aebc2fcce3e482263974077196a6c3fdfe5f381d481"}}