An ensemble view on mixup

Deep ensembles are widely used to improve the generalization, calibration, uncertainty estimates and adversarial robustness of neural networks. In parallel, the data augmentation technique of mixup has grown popular for the very same reasons. Could these two techniques be related? This work suggests that both implement a similar inductive bias to “linearize” decision boundaries. We show how to obtain diverse predictions from a single mixup machine by interpolating a test instance with multiple reference points. These “mixup ensembles” are cheap: one needs to train and store one single model, as opposed to the K independent members forming a deep ensemble. Motivated by the limitations of ensembles to model uncertainty far away from the training data, we propose a variant of mixup that builds augmented examples using both random interpolations and extrapolations of examples. We evaluate the efficacy of our proposed methods across a variety of in-domain and out-domain metrics on the CIFAR-10 and CIFAR-10-NEG datasets.