{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:L7UD7AI3WQHTYNJCGXAAH2EON2","short_pith_number":"pith:L7UD7AI3","schema_version":"1.0","canonical_sha256":"5fe83f811bb40f3c352235c003e88e6eac7375a06dd6eb775a726d74dad68f6f","source":{"kind":"arxiv","id":"2009.05739","version":2},"attestation_state":"computed","paper":{"title":"Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Chenxu Wang, Florian Metze, Hao Xu, Jixuan Gu, Juncheng Li, Xinjian Li, Ze Cheng","submitted_at":"2020-09-12T07:31:30Z","abstract_excerpt":"In the problem of learning disentangled representations, one of the promising methods is to factorize aggregated posterior by penalizing the total correlation of sampled latent variables. However, this well-motivated strategy has a blind spot: there is a disparity between the sampled latent representation and its corresponding mean representation. In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation. Indeed, we prove that for the multivariate normal distributions, the mean repr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2009.05739","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-12T07:31:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a5be9ca7cabdbd9efef1d59fc81da35830d80bc640c7ea0d3dc7e40bb4fb1ab2","abstract_canon_sha256":"1d5d779960a1991e7972157fccbd3904a21cd2a62e799eba030df8481c808d70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:38:30.255061Z","signature_b64":"KGQyLv+QhRj2jEp0hVqwRRbafOx2vnhRsptRxKW5o3IF+Y8Vd1/1ss/GoL0ENKEtLHShVEklfyk6ttdFwfFdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fe83f811bb40f3c352235c003e88e6eac7375a06dd6eb775a726d74dad68f6f","last_reissued_at":"2026-07-05T02:38:30.254617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:38:30.254617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Chenxu Wang, Florian Metze, Hao Xu, Jixuan Gu, Juncheng Li, Xinjian Li, Ze Cheng","submitted_at":"2020-09-12T07:31:30Z","abstract_excerpt":"In the problem of learning disentangled representations, one of the promising methods is to factorize aggregated posterior by penalizing the total correlation of sampled latent variables. However, this well-motivated strategy has a blind spot: there is a disparity between the sampled latent representation and its corresponding mean representation. In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation. Indeed, we prove that for the multivariate normal distributions, the mean repr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.05739","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/2009.05739/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2009.05739","created_at":"2026-07-05T02:38:30.254676+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.05739v2","created_at":"2026-07-05T02:38:30.254676+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.05739","created_at":"2026-07-05T02:38:30.254676+00:00"},{"alias_kind":"pith_short_12","alias_value":"L7UD7AI3WQHT","created_at":"2026-07-05T02:38:30.254676+00:00"},{"alias_kind":"pith_short_16","alias_value":"L7UD7AI3WQHTYNJC","created_at":"2026-07-05T02:38:30.254676+00:00"},{"alias_kind":"pith_short_8","alias_value":"L7UD7AI3","created_at":"2026-07-05T02:38:30.254676+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2","json":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2.json","graph_json":"https://pith.science/api/pith-number/L7UD7AI3WQHTYNJCGXAAH2EON2/graph.json","events_json":"https://pith.science/api/pith-number/L7UD7AI3WQHTYNJCGXAAH2EON2/events.json","paper":"https://pith.science/paper/L7UD7AI3"},"agent_actions":{"view_html":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2","download_json":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2.json","view_paper":"https://pith.science/paper/L7UD7AI3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.05739&json=true","fetch_graph":"https://pith.science/api/pith-number/L7UD7AI3WQHTYNJCGXAAH2EON2/graph.json","fetch_events":"https://pith.science/api/pith-number/L7UD7AI3WQHTYNJCGXAAH2EON2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2/action/storage_attestation","attest_author":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2/action/author_attestation","sign_citation":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2/action/citation_signature","submit_replication":"https://pith.science/pith/L7UD7AI3WQHTYNJCGXAAH2EON2/action/replication_record"}},"created_at":"2026-07-05T02:38:30.254676+00:00","updated_at":"2026-07-05T02:38:30.254676+00:00"}