[{"year":"2023","date_created":"2023-08-22T14:19:03Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2026-07-29T08:02:05Z","volume":36,"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2023-12-16","location":"New Orleans, LA, United States","start_date":"2023-12-10"},"date_published":"2023-12-10T00:00:00Z","ddc":["000"],"supplementarymaterial":"no","abstract":[{"text":"Research on recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming that each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations, but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings.","lang":"eng"}],"das_tickbox":"0","alternative_title":["Advances in Neural Information Processing Systems"],"publication_status":"published","department":[{"_id":"FrLo"}],"external_id":{"arxiv":["2304.07939"]},"file":[{"creator":"dernst","file_size":1466102,"date_updated":"2026-07-29T07:58:19Z","file_name":"2026_Neurips_Fumero.pdf","access_level":"open_access","relation":"main_file","file_id":"22604","checksum":"c70e8a74e205528750c71996ba39c6c1","content_type":"application/pdf","success":1,"date_created":"2026-07-29T07:58:19Z"}],"intvolume":"        36","publication":"37th International Conference on Neural Information Processing Systems","file_date_updated":"2026-07-29T07:58:19Z","month":"12","quality_controlled":"1","publication_identifier":{"issn":["1049-5258"]},"article_number":"1204","publisher":"Neural Information Processing Systems Foundation","day":"10","type":"conference","OA_place":"publisher","status":"public","oa":1,"language":[{"iso":"eng"}],"author":[{"first_name":"Marco","full_name":"Fumero, Marco","last_name":"Fumero"},{"first_name":"Florian","last_name":"Wenzel","full_name":"Wenzel, Florian"},{"first_name":"Luca","last_name":"Zancato","full_name":"Zancato, Luca"},{"full_name":"Achille, Alessandro","last_name":"Achille","first_name":"Alessandro"},{"full_name":"Rodolà, Emanuele","last_name":"Rodolà","first_name":"Emanuele"},{"first_name":"Stefano","full_name":"Soatto, Stefano","last_name":"Soatto"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"}],"article_processing_charge":"No","_id":"14210","corr_author":"1","acknowledgement":"Marco Fumero and Emanuele Rodolà were supported by the ERC grant no.802554 (SPECGEO),\r\nPRIN 2020 project no.2020TA3K9N (LEGO.AI), and PNRR MUR project PE0000013-FAIR. Marco\r\nFumero and Francesco Locatello were partially at Amazon while working at this project. We thank\r\nJulius von Kügelgen, Sebastian Lachapelle and the anonymous reviewers for their feedback and\r\nsuggestions.","oa_version":"Published Version","OA_type":"gold","title":"Leveraging sparse and shared feature activations for disentangled representation learning","arxiv":1,"citation":{"short":"M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, in:, 37th International Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2023.","ieee":"M. Fumero <i>et al.</i>, “Leveraging sparse and shared feature activations for disentangled representation learning,” in <i>37th International Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2023, vol. 36.","ama":"Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. In: <i>37th International Conference on Neural Information Processing Systems</i>. Vol 36. Neural Information Processing Systems Foundation; 2023.","apa":"Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., … Locatello, F. (2023). Leveraging sparse and shared feature activations for disentangled representation learning. In <i>37th International Conference on Neural Information Processing Systems</i> (Vol. 36). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","ista":"Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B, Locatello F. 2023. Leveraging sparse and shared feature activations for disentangled representation learning. 37th International Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 36, 1204.","mla":"Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>37th International Conference on Neural Information Processing Systems</i>, vol. 36, 1204, Neural Information Processing Systems Foundation, 2023.","chicago":"Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” In <i>37th International Conference on Neural Information Processing Systems</i>, Vol. 36. Neural Information Processing Systems Foundation, 2023."},"has_accepted_license":"1","researchdata_availability":"no"}]