{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4EDQ3Z7QD6D7L5XVUB7UIOK6JK","short_pith_number":"pith:4EDQ3Z7Q","canonical_record":{"source":{"id":"1905.10427","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-24T19:57:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3c7bce13db6a4485f3ca1f63f646fe52b1c94bb036f38e684318bfe5cd3c02df","abstract_canon_sha256":"e83929fa33e4a9394aa4dacd86872f86bc4eac6cde5d4b7c72465db3e6c5f580"},"schema_version":"1.0"},"canonical_sha256":"e1070de7f01f87f5f6f5a07f44395e4aa48211f75836010616e159a169ad48f9","source":{"kind":"arxiv","id":"1905.10427","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10427","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10427v2","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10427","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"4EDQ3Z7QD6D7","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_16","alias_value":"4EDQ3Z7QD6D7L5XV","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_8","alias_value":"4EDQ3Z7Q","created_at":"2026-07-05T00:10:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4EDQ3Z7QD6D7L5XVUB7UIOK6JK","target":"record","payload":{"canonical_record":{"source":{"id":"1905.10427","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-24T19:57:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3c7bce13db6a4485f3ca1f63f646fe52b1c94bb036f38e684318bfe5cd3c02df","abstract_canon_sha256":"e83929fa33e4a9394aa4dacd86872f86bc4eac6cde5d4b7c72465db3e6c5f580"},"schema_version":"1.0"},"canonical_sha256":"e1070de7f01f87f5f6f5a07f44395e4aa48211f75836010616e159a169ad48f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:10:01.654705Z","signature_b64":"ZxETZvwevp4A5hpLsU0CSjcK2M8J83I8ru4oVWXEiJ8MoK1gu8TMJ6vtwXIKqKriWHhRX37qqX4IZCS8EsOOBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1070de7f01f87f5f6f5a07f44395e4aa48211f75836010616e159a169ad48f9","last_reissued_at":"2026-07-05T00:10:01.654261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:10:01.654261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.10427","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":"2026-07-05T00:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TwHyt+4nVgOeu9v7bYz5//VROYVOwJoIeQ11Pv+RjFMsPr9xk+W2RBQ1Toi4/PRg7gpoqzdyczSf8cHUcVLlBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:38:45.616942Z"},"content_sha256":"b94c66d194bd2b07bcf98bcd5edbdfb5f790b92bb397087e9bd8381950cba781","schema_version":"1.0","event_id":"sha256:b94c66d194bd2b07bcf98bcd5edbdfb5f790b92bb397087e9bd8381950cba781"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4EDQ3Z7QD6D7L5XVUB7UIOK6JK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DIVA: Domain Invariant Variational Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christos Louizos, Jakub M. Tomczak, Maximilian Ilse, Max Welling","submitted_at":"2019-05-24T19:57:39Z","abstract_excerpt":"We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations. We highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. To the best of our knowledge this has not been don"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10427","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/1905.10427/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":"2026-07-05T00:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cj67h2yEtsiRl2Wpw+P8dd2cewgN/+ZHP7zpm70oVzvOLVf/2QtIKr3hn2GaZVvpHQPVCr+zVyvGQK8tM9mnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:38:45.617449Z"},"content_sha256":"2c6b03d83d625b36a70050ff812668d6e486558d398f19ed2c8cabe30bd9fd36","schema_version":"1.0","event_id":"sha256:2c6b03d83d625b36a70050ff812668d6e486558d398f19ed2c8cabe30bd9fd36"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/bundle.json","state_url":"https://pith.science/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/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":"2026-08-11T03:38:45Z","links":{"resolver":"https://pith.science/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK","bundle":"https://pith.science/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/bundle.json","state":"https://pith.science/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4EDQ3Z7QD6D7L5XVUB7UIOK6JK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4EDQ3Z7QD6D7L5XVUB7UIOK6JK","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":"e83929fa33e4a9394aa4dacd86872f86bc4eac6cde5d4b7c72465db3e6c5f580","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-24T19:57:39Z","title_canon_sha256":"3c7bce13db6a4485f3ca1f63f646fe52b1c94bb036f38e684318bfe5cd3c02df"},"schema_version":"1.0","source":{"id":"1905.10427","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10427","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10427v2","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10427","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"4EDQ3Z7QD6D7","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_16","alias_value":"4EDQ3Z7QD6D7L5XV","created_at":"2026-07-05T00:10:01Z"},{"alias_kind":"pith_short_8","alias_value":"4EDQ3Z7Q","created_at":"2026-07-05T00:10:01Z"}],"graph_snapshots":[{"event_id":"sha256:2c6b03d83d625b36a70050ff812668d6e486558d398f19ed2c8cabe30bd9fd36","target":"graph","created_at":"2026-07-05T00:10:01Z","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/1905.10427/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations. We highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. To the best of our knowledge this has not been don","authors_text":"Christos Louizos, Jakub M. Tomczak, Maximilian Ilse, Max Welling","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-24T19:57:39Z","title":"DIVA: Domain Invariant Variational Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10427","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:b94c66d194bd2b07bcf98bcd5edbdfb5f790b92bb397087e9bd8381950cba781","target":"record","created_at":"2026-07-05T00:10:01Z","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":"e83929fa33e4a9394aa4dacd86872f86bc4eac6cde5d4b7c72465db3e6c5f580","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-24T19:57:39Z","title_canon_sha256":"3c7bce13db6a4485f3ca1f63f646fe52b1c94bb036f38e684318bfe5cd3c02df"},"schema_version":"1.0","source":{"id":"1905.10427","kind":"arxiv","version":2}},"canonical_sha256":"e1070de7f01f87f5f6f5a07f44395e4aa48211f75836010616e159a169ad48f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1070de7f01f87f5f6f5a07f44395e4aa48211f75836010616e159a169ad48f9","first_computed_at":"2026-07-05T00:10:01.654261Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:10:01.654261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZxETZvwevp4A5hpLsU0CSjcK2M8J83I8ru4oVWXEiJ8MoK1gu8TMJ6vtwXIKqKriWHhRX37qqX4IZCS8EsOOBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:10:01.654705Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.10427","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b94c66d194bd2b07bcf98bcd5edbdfb5f790b92bb397087e9bd8381950cba781","sha256:2c6b03d83d625b36a70050ff812668d6e486558d398f19ed2c8cabe30bd9fd36"],"state_sha256":"8a15b7fe8157bdd9e82d44e0bbca6b3fdd724a90c136d651a847e3323ccf2059"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6uaDq3G6W5vaQKCk/Diu1dzqAblZQP3GhRvdiR/IEp9xoCy8lLyajatys0Rw6pmI94FBERzrHGuBCSIYcyqSDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T03:38:45.622605Z","bundle_sha256":"41022dc65e4ce9439ce790b77d05d310441630ef9427c7851ea1f43de635c7d0"}}