Adversarial Transformations for Semi-Supervised Learning
This work addresses semi-supervised image classification, offering an incremental improvement over prior regularization techniques.
The paper tackled the problem of improving semi-supervised learning by enhancing robustness against input perturbations, resulting in significant performance gains on CIFAR-10 and SVHN compared to existing methods.
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.