{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/disentangling-shared-and-private-latent","title":"Disentangling shared and private latent factors in multimodal Variational Autoencoders","arxiv_id":"2403.06338","date":"2024-03-10","proceeding":null,"authors":["Kaspar Märtens","Christopher Yau"],"abstract":"Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality. Multimodal Variational Autoencoders, such as MVAE and MMVAE, are a natural choice for inferring those underlying latent factors and separating shared variation from private. In this work, we investigate their capability to reliably perform this disentanglement. In particular, we highlight a challenging problem setting where modality-specific variation dominates the shared signal. Taking a cross-modal prediction perspective, we demonstrate limitations of existing models, and propose a modification how to make them more robust to modality-specific variation. Our findings are supported by experiments on synthetic as well as various real-world multi-omics data sets.","url_abs":"https://arxiv.org/abs/2403.06338v1","url_pdf":"https://arxiv.org/pdf/2403.06338v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"disentangling-shared-and-private-latent","repo_url":"https://github.com/kasparmartens/shared-private-multimodalvae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2403.06338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06338"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kasparmartens/shared-private-multimodalvae","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":8,"unverified":3},"by_repo_kind":{"official":{"samples":11,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"59b0de25e1b1d43f","entry":"KL_qp_like","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"59b0de25e1b1d43f"}},{"code_sha256_prefix":"1fe833712d4674e7","entry":"calculate_Rsq","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/eval_utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/eval_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1fe833712d4674e7"}},{"code_sha256_prefix":"133587a6548c13d6","entry":"calculate_Rsq_numpy","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/eval_utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/eval_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"133587a6548c13d6"}},{"code_sha256_prefix":"dbe3e8e228856257","entry":"calculate_Rsq_torch","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/eval_utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/eval_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dbe3e8e228856257"}},{"code_sha256_prefix":"32482040d0fa5afd","entry":"generate_from_GP","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"dataset/generate_toy_data.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/dataset/generate_toy_data.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"32482040d0fa5afd"}},{"code_sha256_prefix":"93d8ad3059982f4b","entry":"generate_from_MVN","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"dataset/generate_toy_data.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/dataset/generate_toy_data.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"93d8ad3059982f4b"}},{"code_sha256_prefix":"0f05f2c144dfe579","entry":"get_latent_structure","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/latent_structure_utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/latent_structure_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0f05f2c144dfe579"}},{"code_sha256_prefix":"630d7e83cca45eba","entry":"subset_normal","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/utils.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"630d7e83cca45eba"}},{"code_sha256_prefix":"4aa90fc9fa18ace9","entry":"RBF","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"dataset/generate_toy_data.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/dataset/generate_toy_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4aa90fc9fa18ace9"}},{"code_sha256_prefix":"46537ff45296c1ef","entry":"get_MOFA_CLL_data_IGHV","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"dataset/load_data.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/dataset/load_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"46537ff45296c1ef"}},{"code_sha256_prefix":"bd03a01c41cd4a31","entry":"helper_fit_logreg","repo":"kasparmartens/shared-private-multimodalvae","repo_kind":"official","path":"multimodalVAE/linear_classifier.py","file_url":"https://github.com/kasparmartens/shared-private-multimodalvae/blob/HEAD/multimodalVAE/linear_classifier.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bd03a01c41cd4a31"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}