Max Planck Institute for Intelligent Systems Tübingen, University of Cambridge; Tübingen AI Center, University of Tübingen, IMPRS for Intelligent Systems; Max Planck Institute for Intelligent Systems Tübingen; Tübingen AI Center, University of Tübingen; Amazon
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
self-supervised learning;data augmentation;identifiability;nonlinear ICA;content style separation;causal representation learning;multi-view learning
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style