Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
This work addresses the challenge of computational inefficiency in adversarial augmentation for domain adaptation and generalization, offering a practical solution for researchers and practitioners in computer vision.
The paper tackles the problem of improving domain adaptation and generalization in deep neural networks by proposing a method that combines consistency training with random data augmentation and a differentiable adversarial augmentation using spatial transformer networks, achieving state-of-the-art results on multiple benchmark datasets with demonstrated robustness to corruption.
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve the accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STN). The combined adversarial and random transformations based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Besides, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.