Randomized Adversarial Style Perturbations for Domain Generalization
This addresses domain generalization for machine learning models to perform better on unseen target domains, representing an incremental improvement with a hybrid method.
The paper tackles domain generalization by proposing Randomized Adversarial Style Perturbation (RASP) to handle domain shifts, combined with Normalized Feature Mixup (NFM) to maintain learning from source domains, showing improved performance on large-scale benchmarks.
We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP), which is motivated by the observation that the characteristics of each domain are captured by the feature statistics corresponding to style. The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly selected class, and makes the model learn against being misled by the unexpected styles observed in unseen target domains. While RASP is effective to handle domain shifts, its naive integration into the training procedure might degrade the capability of learning knowledge from source domains because it has no restriction on the perturbations of representations. This challenge is alleviated by Normalized Feature Mixup (NFM), which facilitates the learning of the original features while achieving robustness to perturbed representations via their mixup during training. We evaluate the proposed algorithm via extensive experiments on various benchmarks and show that our approach improves domain generalization performance, especially in large-scale benchmarks.