  MixUp is a recently proposed data-augmentation scheme, which linearly
interpolates a random pair of training examples and correspondingly the one-hot
representations of their labels. Training deep neural networks with such
additional data is shown capable of significantly improving the predictive
accuracy of the current art. The power of MixUp, however, is primarily
established empirically and its working and effectiveness have not been
explained in any depth. In this paper, we develop an understanding for MixUp as
a form of "out-of-manifold regularization", which imposes certain "local
linearity" constraints on the model's input space beyond the data manifold.
This analysis enables us to identify a limitation of MixUp, which we call
"manifold intrusion". In a nutshell, manifold intrusion in MixUp is a form of
under-fitting resulting from conflicts between the synthetic labels of the
mixed-up examples and the labels of original training data. Such a phenomenon
usually happens when the parameters controlling the generation of mixing
policies are not sufficiently fine-tuned on the training data. To address this
issue, we propose a novel adaptive version of MixUp, where the mixing policies
are automatically learned from the data using an additional network and
objective function designed to avoid manifold intrusion. The proposed
regularizer, AdaMixUp, is empirically evaluated on several benchmark datasets.
Extensive experiments demonstrate that AdaMixUp improves upon MixUp when
applied to the current art of deep classification models.
