Title: Bypassing the Random Input Mixing in Mixup. Abstract: Mixup and its variants have promoted a surge of interest due to their capability of boosting the accuracy of deep  models. For a random sample pair, such approaches generate a set of synthetic samples through interpolating both the  inputs and their corresponding one-hot labels. Current methods either interpolate random features from an input pair or learn to mix salient features  from the  pair. Nevertheless, the former methods can create misleading synthetic    samples or remove important features from the given inputs, and the latter strategies incur significant computation cost for selecting descriptive input regions. In this paper, we show that the effort needed for the input mixing can be bypassed. For a given sample pair, averaging the features from the two inputs and then assigning it with a set of  soft labels can  effectively regularize the training. We empirically show that the proposed  approach performs on par with state-of-the-art strategies in terms of predictive accuracy. 