  We propose a Regularization framework based on Adversarial Transformations
(RAT) for semi-supervised learning. RAT is designed to enhance robustness of
the output distribution of class prediction for a given data against input
perturbation. RAT is an extension of Virtual Adversarial Training (VAT) in such
a way that RAT adversarialy transforms data along the underlying data
distribution by a rich set of data transformation functions that leave class
label invariant, whereas VAT simply produces adversarial additive noises. In
addition, we verified that a technique of gradually increasing of perturbation
region further improve the robustness. In experiments, we show that RAT
significantly improves classification performance on CIFAR-10 and SVHN compared
to existing regularization methods under standard semi-supervised image
classification settings.
