MixUp as Locally Linear Out-Of-Manifold Regularization

Understand

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.