Schott, Lukas, von Kügelgen, Julius, Träuble, Frederik, Gehler, Peter, Russell, Chris, Bethge, Matthias, Schölkopf, Bernhard, Locatello, Francesco, Brendel, Wieland Visual representation learning does not generalize strongly within the same domain In Tenth International Conference on Learning Representations (ICLR 2022), 2021 (inproceedings)

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Zimmermann, Roland S, Sharma, Yash, Schneider, Steffen, Bethge, Matthias, Brendel, Wieland Contrastive Learning Inverts the Data Generating Process In International Conference on Machine Learning (ICML 2021), 2021 (inproceedings)

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von Kügelgen, Julius, Sharma, Yash, Gresele, Luigi, Brendel, Wieland, Schölkopf, Bernhard, Besserve, Michel, Locatello, Francesco Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Pintor, Maura, Roli, Fabio, Brendel, Wieland, Biggio, Battista Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Geirhos, Robert, Narayanappa, Kantharaju, Mitzkus, Benjamin, Thieringer, Tizian, Bethge, Matthias, Wichmann, Felix A, Brendel, Wieland Partial success in closing the gap between human and machine vision In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Funke, Christina M, Borowski, Judy, Stosio, Karolina, Brendel, Wieland, Wallis, Thomas SA, Bethge, Matthias Five points to check when comparing visual perception in humans and machines Journal of Vision, 21(3):16-16, The Association for Research in Vision and Ophthalmology, 2021 (article)

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Zimmermann, Roland S, Borowski, Judy, Geirhos, Robert, Bethge, Matthias, Wallis, Thomas SA, Brendel, Wieland How Well do Feature Visualizations Support Causal Understanding of CNN Activations? In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Rusak, Evgenia, Schneider, Steffen, Pachitariu, George, Eck, Luisa, Gehler, Peter Vincent, Bringmann, Oliver, Brendel, Wieland, Bethge, Matthias If your data distribution shifts, use self-learning 2021 (article)

[BibTex]

Rusak, Evgenia, Schneider, Steffen, Gehler, Peter, Bringmann, Oliver, Brendel, Wieland, Bethge, Matthias Adapting ImageNet-scale models to complex distribution shifts with self-learning arXiv preprint arXiv:2104.12928, 2021 (article)

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