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We show that the effectiveness of the well celebrated Mixup [Zhang et al.,2018] can be further improved if instead of using it as the sole learningobjective, it is utilized as an additional regularizer to the standardcross-entropy loss. This simple change not only provides much improved accuracybut also significantly improves the quality of the predictive uncertaintyestimation of Mixup in most cases under various forms of covariate shifts andout-of-distribution detection experiments. In fact, we observe that Mixupyields much degraded performance on detecting out-of-distribution samplespossibly, as we show empirically, because of its tendency to learn models thatexhibit high-entropy throughout; making it difficult to differentiatein-distribution samples from out-distribution ones. To show the efficacy of ourapproach (RegMixup), we provide thorough analyses and experiments on visiondatasets (ImageNet & CIFAR-10/100) and compare it with a suite of recentapproaches for reliable uncertainty estimation.

RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness