University of Tübingen, Tübingen AI Center; University of Tübingen, Tübingen AI Center, Max-Planck-Institute for Intelligent Systems, Tübingen; Tübingen AI Center, Max-Planck-Institute for Intelligent Systems, Tübingen, ELLIS Institute Tübingen
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
robustness;foundation models;CLIP;LAION;ImageNet;generalization;OOD robustness;distribution shift;vision language models;self-supervised learning;contrastive learning;ObjectNet;ImageNet-R;ImageNet-Sketch;ImageNet-A;ImageNet-V2
Does CLIP’s generalization performance mainly stem from high train-test similarity?