Web: http://arxiv.org/abs/2209.05104

Sept. 15, 2022, 1:11 a.m. | S Chandra Mouli, Yangze Zhou, Bruno Ribeiro

cs.LG updates on arXiv.org arxiv.org

Deep learning models tend not to be out-of-distribution robust primarily due
to their reliance on spurious features to solve the task. Counterfactual data
augmentations provide a general way of (approximately) achieving
representations that are counterfactual-invariant to spurious features, a
requirement for out-of-distribution (OOD) robustness. In this work, we show
that counterfactual data augmentations may not achieve the desired
counterfactual-invariance if the augmentation is performed by a
context-guessing machine, an abstract machine that guesses the most-likely
context of a given input. …

arxiv augmentation bias challenges data

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