{"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/identifiability-results-for-multimodal","title":"Identifiability Results for Multimodal Contrastive Learning","arxiv_id":"2303.09166","date":"2023-03-16","proceeding":null,"authors":["Imant Daunhawer","Alice Bizeul","Emanuele Palumbo","Alexander Marx","Julia E. Vogt"],"abstract":"Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effectiveness is not yet fully understood, a line of recent work reveals that contrastive learning can invert the data generating process and recover ground truth latent factors shared between views. In this work, we present new identifiability results for multimodal contrastive learning, showing that it is possible to recover shared factors in a more general setup than the multi-view setting studied previously. Specifically, we distinguish between the multi-view setting with one generative mechanism (e.g., multiple cameras of the same type) and the multimodal setting that is characterized by distinct mechanisms (e.g., cameras and microphones). Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables. We prove that contrastive learning can block-identify latent factors shared between modalities, even when there are nontrivial dependencies between factors. We empirically verify our identifiability results with numerical simulations and corroborate our findings on a complex multimodal dataset of image/text pairs. Zooming out, our work provides a theoretical basis for multimodal representation learning and explains in which settings multimodal contrastive learning can be effective in practice.","url_abs":"https://arxiv.org/abs/2303.09166v1","url_pdf":"https://arxiv.org/pdf/2303.09166v1.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":"identifiability-results-for-multimodal","repo_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.09166","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09166"}},"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/imantdaunhawer/multimodal-contrastive-learning","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":2,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"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":5,"samples":[{"code_sha256_prefix":"fc680d8d1ece352b","entry":"generate_data","repo":"imantdaunhawer/multimodal-contrastive-learning","repo_kind":"official","path":"main_mlp.py","file_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning/blob/HEAD/main_mlp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"fc680d8d1ece352b"}},{"code_sha256_prefix":"600f5a2225c1b02c","entry":"train_step","repo":"imantdaunhawer/multimodal-contrastive-learning","repo_kind":"official","path":"main_imgtxt.py","file_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning/blob/HEAD/main_imgtxt.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"600f5a2225c1b02c"}},{"code_sha256_prefix":"0210fa76308c86c0","entry":"val_step","repo":"imantdaunhawer/multimodal-contrastive-learning","repo_kind":"official","path":"main_imgtxt.py","file_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning/blob/HEAD/main_imgtxt.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"0210fa76308c86c0"}},{"code_sha256_prefix":"dfab8c9988904534","entry":"val_step","repo":"imantdaunhawer/multimodal-contrastive-learning","repo_kind":"official","path":"main_mlp.py","file_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning/blob/HEAD/main_mlp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"dfab8c9988904534"}},{"code_sha256_prefix":"5b044fb11a2bde22","entry":"train_step","repo":"imantdaunhawer/multimodal-contrastive-learning","repo_kind":"official","path":"main_mlp.py","file_url":"https://github.com/imantdaunhawer/multimodal-contrastive-learning/blob/HEAD/main_mlp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5b044fb11a2bde22"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}