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 learning objective, it is utilized as an additional regularizer to the standard cross-entropy loss. This simple change not only provides much improved accuracy but also significantly improves the quality of the predictive uncertainty estimation of Mixup in most cases under various forms of covariate shifts and out-of-distribution detection experiments. In fact, we observe that Mixup yields much degraded performance on detecting out-of-distribution samples possibly, as we show empirically, because of its tendency to learn models that exhibit high-entropy throughout; making it difficult to differentiate in-distribution samples from out-distribution ones. To show the efficacy of our approach (RegMixup), we provide thorough analyses and experiments on vision datasets (ImageNet & CIFAR-10/100) and compare it with a suite of recent approaches for reliable uncertainty estimation.

利用 Mixup 作为标准交叉熵损失的额外正则化器，可以提高其精确度并在各种协变量转移和超出分布检测实验中显著提高其预测不确定性的质量。通过对视觉数据集 (ImageNet ＆ CIFAR-10/100) 的深入分析和实验来展示我们的方法（RegMixup）的功效，并与一系列最新方法进行比较，以实现可靠的不确定性估计。

RegMixup：使用Mixup作为正则化器，出乎意料地可以提高精度和外部数据稳健性