If your data distribution shifts, use self-learning

Evgenia Rusak; Steffen Schneider; George Pachitariu; Luisa Eck; Peter Gehler; Oliver Bringmann; Wieland Brendel; Matthias Bethge

arXiv · 2104.12928

If your data distribution shifts, use self-learning

Evgenia Rusak·Steffen Schneider·George Pachitariu·Luisa Eck·Peter Gehler·Oliver Bringmann·Wieland Brendel·Matthias Bethge

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Abstract

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. We conduct a wide range of large-scale experiments and show consistent improvements irrespective of the model architecture, the pre-training technique or the type of distribution shift. At the same time, self-learning is simple to use in practice because it does not require knowledge or access to the original training data or scheme, is robust to hyperparameter choices, is straight-forward to implement and requires only a few adaptation epochs. This makes self-learning techniques highly attractive for any practitioner who applies machine learning algorithms in the real world. We present state-of-the-art adaptation results on CIFAR10-C (8.5% error), ImageNet-C (22.0% mCE), ImageNet-R (17.4% error) and ImageNet-A (14.8% error), theoretically study the dynamics of self-supervised adaptation methods and propose a new classification dataset (ImageNet-D) which is challenging even with adaptation.

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Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge. 2021-04-27. If your data distribution shifts, use self-learning. https://arxiv.org/abs/2104.12928

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