1 code implementation • 9 Jan 2024 • Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
Using a simple and intuitive complexity measure, we are able to reduce the training cost to a quarter of regular training.
1 code implementation • 14 Oct 2023 • Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
Foundation models like CLIP are trained on hundreds of millions of samples and effortlessly generalize to new tasks and inputs.
1 code implementation • 27 Apr 2021 • Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge
We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.
Ranked #1 on Unsupervised Domain Adaptation on ImageNet-A (using extra training data)
2 code implementations • NeurIPS 2020 • Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
With the more robust DeepAugment+AugMix model, we improve the state of the art achieved by a ResNet50 model up to date from 53. 6% mCE to 45. 4% mCE.
Ranked #4 on Unsupervised Domain Adaptation on ImageNet-R
3 code implementations • ECCV 2020 • Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.
4 code implementations • 17 Jul 2019 • Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on Robust Object Detection on MS COCO