  Mixup and its variants form a popular class of data augmentation
techniques.Using a random sample pair, it generates a new sample by linear
interpolation of the inputs and labels. However, generating only one single
interpolation may limit its augmentation ability. In this paper, we propose a
simple yet effective extension called multi-mix, which generates multiple
interpolations from a sample pair. With an ordered sequence of generated
samples, multi-mix can better guide the training process than standard mixup.
Moreover, theoretically, this can also reduce the stochastic gradient variance.
Extensive experiments on a number of synthetic and large-scale data sets
demonstrate that multi-mix outperforms various mixup variants and
non-mixup-based baselines in terms of generalization, robustness, and
calibration.
