Robustness via Cross-Domain Ensembles

Teresa Yeo, Oğuzhan Fatih Kar, Amir Zamir; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12189-12199


We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called `middle domains') and ensembling them into one strong prediction. The premise of the idea is that predictions made via different cues respond differently to a distribution shift, hence one should be able to merge them into one robust final prediction. We perform the merging in a straightforward but principled manner based on the uncertainty associated with each prediction. The evaluations are performed using multiple tasks and datasets (Taskonomy, Replica, ImageNet, CIFAR) under a wide range of adversarial and non-adversarial distribution shifts which demonstrate the proposed method is considerably more robust than its standard learning counterpart, conventional deep ensembles, and several other baselines.

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[pdf] [arXiv]
@InProceedings{Yeo_2021_ICCV, author = {Yeo, Teresa and Kar, O\u{g}uzhan Fatih and Zamir, Amir}, title = {Robustness via Cross-Domain Ensembles}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {12189-12199} }