{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CZROBKF7A6BPWYV226F242O5YZ","short_pith_number":"pith:CZROBKF7","canonical_record":{"source":{"id":"1810.02845","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-05T18:42:02Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"4041efc68deed3a35ee386ef462ffa8d138d08da85b026b0c1efac4419d2fb01","abstract_canon_sha256":"cccdafcbd1ded219588cfe2cee156e5a19e8861c5b1f8f899d32e675f35fb09c"},"schema_version":"1.0"},"canonical_sha256":"1662e0a8bf0782fb62bad78bae69ddc672ab8a33cdf090ffd69705a1fd812de1","source":{"kind":"arxiv","id":"1810.02845","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.02845","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"1810.02845v2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.02845","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"CZROBKF7A6BP","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"CZROBKF7A6BPWYV2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"CZROBKF7","created_at":"2026-07-05T00:16:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CZROBKF7A6BPWYV226F242O5YZ","target":"record","payload":{"canonical_record":{"source":{"id":"1810.02845","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-05T18:42:02Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"4041efc68deed3a35ee386ef462ffa8d138d08da85b026b0c1efac4419d2fb01","abstract_canon_sha256":"cccdafcbd1ded219588cfe2cee156e5a19e8861c5b1f8f899d32e675f35fb09c"},"schema_version":"1.0"},"canonical_sha256":"1662e0a8bf0782fb62bad78bae69ddc672ab8a33cdf090ffd69705a1fd812de1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:16:31.387510Z","signature_b64":"G+5obB+h1nktLmL5/hHjwn5UgtngWbBsbq/ZXOwPIhN7ot3L3CsRIriRFwwzt1fkM3LTPWc1NxFA3Np861aEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1662e0a8bf0782fb62bad78bae69ddc672ab8a33cdf090ffd69705a1fd812de1","last_reissued_at":"2026-07-05T00:16:31.387068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:16:31.387068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.02845","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":"2026-07-05T00:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uhDBQCZmKZ0CMIMECNvzcJs635m0xC4Ki2snTv39ekZHaRXupa8j2GMKCZ9fZcP3eI0uZpR1NtLSjduVdhJlCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T20:54:29.508834Z"},"content_sha256":"c3a7e6006702dcc44e58cd132c2bd7a4b6cfd019ccc5ae6f85fe564a2a635d87","schema_version":"1.0","event_id":"sha256:c3a7e6006702dcc44e58cd132c2bd7a4b6cfd019ccc5ae6f85fe564a2a635d87"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CZROBKF7A6BPWYV226F242O5YZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Generative Video Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christopher Schroers, Jun Han, Salvator Lombardo, Stephan Mandt","submitted_at":"2018-10-05T18:42:02Z","abstract_excerpt":"The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this represen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.02845","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1810.02845/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":"2026-07-05T00:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7or1o8Qfw3B7BEw9Xg+GmLA1jBYk2qZYUmBl7LT+SNOq+biqsx3F4q/Y+bC88W98kTvWqtxme5PvCWEpQ3mcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T20:54:29.509838Z"},"content_sha256":"daaa05d3fd27646b633678927a6a99c25fcab50611ff1ac29233328aba0f001d","schema_version":"1.0","event_id":"sha256:daaa05d3fd27646b633678927a6a99c25fcab50611ff1ac29233328aba0f001d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CZROBKF7A6BPWYV226F242O5YZ/bundle.json","state_url":"https://pith.science/pith/CZROBKF7A6BPWYV226F242O5YZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CZROBKF7A6BPWYV226F242O5YZ/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":"2026-08-14T20:54:29Z","links":{"resolver":"https://pith.science/pith/CZROBKF7A6BPWYV226F242O5YZ","bundle":"https://pith.science/pith/CZROBKF7A6BPWYV226F242O5YZ/bundle.json","state":"https://pith.science/pith/CZROBKF7A6BPWYV226F242O5YZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CZROBKF7A6BPWYV226F242O5YZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CZROBKF7A6BPWYV226F242O5YZ","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":"cccdafcbd1ded219588cfe2cee156e5a19e8861c5b1f8f899d32e675f35fb09c","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-05T18:42:02Z","title_canon_sha256":"4041efc68deed3a35ee386ef462ffa8d138d08da85b026b0c1efac4419d2fb01"},"schema_version":"1.0","source":{"id":"1810.02845","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.02845","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"1810.02845v2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.02845","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"CZROBKF7A6BP","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"CZROBKF7A6BPWYV2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"CZROBKF7","created_at":"2026-07-05T00:16:31Z"}],"graph_snapshots":[{"event_id":"sha256:daaa05d3fd27646b633678927a6a99c25fcab50611ff1ac29233328aba0f001d","target":"graph","created_at":"2026-07-05T00:16:31Z","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/1810.02845/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this represen","authors_text":"Christopher Schroers, Jun Han, Salvator Lombardo, Stephan Mandt","cross_cats":["cs.LG","eess.IV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-05T18:42:02Z","title":"Deep Generative Video Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.02845","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:c3a7e6006702dcc44e58cd132c2bd7a4b6cfd019ccc5ae6f85fe564a2a635d87","target":"record","created_at":"2026-07-05T00:16:31Z","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":"cccdafcbd1ded219588cfe2cee156e5a19e8861c5b1f8f899d32e675f35fb09c","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-05T18:42:02Z","title_canon_sha256":"4041efc68deed3a35ee386ef462ffa8d138d08da85b026b0c1efac4419d2fb01"},"schema_version":"1.0","source":{"id":"1810.02845","kind":"arxiv","version":2}},"canonical_sha256":"1662e0a8bf0782fb62bad78bae69ddc672ab8a33cdf090ffd69705a1fd812de1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1662e0a8bf0782fb62bad78bae69ddc672ab8a33cdf090ffd69705a1fd812de1","first_computed_at":"2026-07-05T00:16:31.387068Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:16:31.387068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G+5obB+h1nktLmL5/hHjwn5UgtngWbBsbq/ZXOwPIhN7ot3L3CsRIriRFwwzt1fkM3LTPWc1NxFA3Np861aEAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:16:31.387510Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.02845","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3a7e6006702dcc44e58cd132c2bd7a4b6cfd019ccc5ae6f85fe564a2a635d87","sha256:daaa05d3fd27646b633678927a6a99c25fcab50611ff1ac29233328aba0f001d"],"state_sha256":"85a326af7881c2500a6fbdbbafdc71f415511ce946ebd162297ca8bb8942fc3b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"itLhm2zrDsT/U5IA0VXiq7aGYLKPpf6v4Rd9PBzDQStfBpiA0MJ0DoKHUExd+Qrar9U6ij9kxvfbxZMk/hJzBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T20:54:29.518890Z","bundle_sha256":"7b707d5ecc24b0468f7b16c290bf1dc1619e7b7562e9329f945d0287de54d705"}}