{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LXYEUDROAQYIUSSDAPKCBHUO2G","short_pith_number":"pith:LXYEUDRO","canonical_record":{"source":{"id":"1711.00305","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-01T12:18:26Z","cross_cats_sorted":[],"title_canon_sha256":"09cbe27b0f9cecca7bcde93ca45004c5c817f78c1253a358f5562dcb5bf44673","abstract_canon_sha256":"8b0c5829c74e70e9cca4c019404ded496ddb3db89e9ba718a0b02455d776dc14"},"schema_version":"1.0"},"canonical_sha256":"5df04a0e2e04308a4a4303d4209e8ed185882b2a03ce0150a79bddd6faf19e0a","source":{"kind":"arxiv","id":"1711.00305","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00305","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00305v2","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00305","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"pith_short_12","alias_value":"LXYEUDROAQYI","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LXYEUDROAQYIUSSD","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LXYEUDRO","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LXYEUDROAQYIUSSDAPKCBHUO2G","target":"record","payload":{"canonical_record":{"source":{"id":"1711.00305","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-01T12:18:26Z","cross_cats_sorted":[],"title_canon_sha256":"09cbe27b0f9cecca7bcde93ca45004c5c817f78c1253a358f5562dcb5bf44673","abstract_canon_sha256":"8b0c5829c74e70e9cca4c019404ded496ddb3db89e9ba718a0b02455d776dc14"},"schema_version":"1.0"},"canonical_sha256":"5df04a0e2e04308a4a4303d4209e8ed185882b2a03ce0150a79bddd6faf19e0a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:22.850578Z","signature_b64":"JO5HofDuZz3JZeyd5Ecu1KNsICdba2qdHziMuX8IzhfBZcNUCrol02gu1AOABy6J9K7Xd4USq2JAuD2q+iQ+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5df04a0e2e04308a4a4303d4209e8ed185882b2a03ce0150a79bddd6faf19e0a","last_reissued_at":"2026-05-17T23:48:22.850150Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:22.850150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.00305","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":"2026-05-17T23:48:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YuuOYRyR3E1uMP1mGE70fs8cDgVh65iQyNKFRCiyV/ugdPCvGS91cmTKTieXJY8NHkyGVNb6bwZNwomXU5fLCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:50:53.305304Z"},"content_sha256":"8fcee7c125f4ae525c6a04a7ee9a2c97e3f79bbbe56aaeaada94cdacf27e2fba","schema_version":"1.0","event_id":"sha256:8fcee7c125f4ae525c6a04a7ee9a2c97e3f79bbbe56aaeaada94cdacf27e2fba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LXYEUDROAQYIUSSDAPKCBHUO2G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-View Data Generation Without View Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ludovic Denoyer, Micka\\\"el Chen, Thierry Arti\\`eres","submitted_at":"2017-11-01T12:18:26Z","abstract_excerpt":"The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00305","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":"2026-05-17T23:48:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5bUtuHixIrghgeYYKdG37vQPH7Xu0rNufNfBQJaHVdhoN5fGg6PIGc432ZafI0VLebUGLaTVUvOuaE1WbnrtCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:50:53.305660Z"},"content_sha256":"b9f8a9a4dd349c1f96aa302d9431aebb0d5bee4f69a6946d19c50ebf0a36952d","schema_version":"1.0","event_id":"sha256:b9f8a9a4dd349c1f96aa302d9431aebb0d5bee4f69a6946d19c50ebf0a36952d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/bundle.json","state_url":"https://pith.science/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/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":"2026-07-06T17:50:53Z","links":{"resolver":"https://pith.science/pith/LXYEUDROAQYIUSSDAPKCBHUO2G","bundle":"https://pith.science/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/bundle.json","state":"https://pith.science/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LXYEUDROAQYIUSSDAPKCBHUO2G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LXYEUDROAQYIUSSDAPKCBHUO2G","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":"8b0c5829c74e70e9cca4c019404ded496ddb3db89e9ba718a0b02455d776dc14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-01T12:18:26Z","title_canon_sha256":"09cbe27b0f9cecca7bcde93ca45004c5c817f78c1253a358f5562dcb5bf44673"},"schema_version":"1.0","source":{"id":"1711.00305","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00305","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00305v2","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00305","created_at":"2026-05-17T23:48:22Z"},{"alias_kind":"pith_short_12","alias_value":"LXYEUDROAQYI","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LXYEUDROAQYIUSSD","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LXYEUDRO","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:b9f8a9a4dd349c1f96aa302d9431aebb0d5bee4f69a6946d19c50ebf0a36952d","target":"graph","created_at":"2026-05-17T23:48:22Z","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":"The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the i","authors_text":"Ludovic Denoyer, Micka\\\"el Chen, Thierry Arti\\`eres","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-01T12:18:26Z","title":"Multi-View Data Generation Without View Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00305","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":"sha256:8fcee7c125f4ae525c6a04a7ee9a2c97e3f79bbbe56aaeaada94cdacf27e2fba","target":"record","created_at":"2026-05-17T23:48:22Z","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":"8b0c5829c74e70e9cca4c019404ded496ddb3db89e9ba718a0b02455d776dc14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-01T12:18:26Z","title_canon_sha256":"09cbe27b0f9cecca7bcde93ca45004c5c817f78c1253a358f5562dcb5bf44673"},"schema_version":"1.0","source":{"id":"1711.00305","kind":"arxiv","version":2}},"canonical_sha256":"5df04a0e2e04308a4a4303d4209e8ed185882b2a03ce0150a79bddd6faf19e0a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5df04a0e2e04308a4a4303d4209e8ed185882b2a03ce0150a79bddd6faf19e0a","first_computed_at":"2026-05-17T23:48:22.850150Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:22.850150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JO5HofDuZz3JZeyd5Ecu1KNsICdba2qdHziMuX8IzhfBZcNUCrol02gu1AOABy6J9K7Xd4USq2JAuD2q+iQ+Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:22.850578Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.00305","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8fcee7c125f4ae525c6a04a7ee9a2c97e3f79bbbe56aaeaada94cdacf27e2fba","sha256:b9f8a9a4dd349c1f96aa302d9431aebb0d5bee4f69a6946d19c50ebf0a36952d"],"state_sha256":"41bb5d8b6d1f4cee18fab9a10111e048471b1972db5f8964b0a85d1138e65f9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EOkseRhfIKI3wy+/mrZJqysFta7IDv0gwnMDr18fZbkgGJCS5zH7wKEoub1OaEwIOB3YE2K76cn11hZU4SS/CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:50:53.307577Z","bundle_sha256":"ca22002c6e91eef3184a27aea7322d21da83b9b6fb88d5fc2557e1793050dfb0"}}