Generalizing to Unseen Domains via Adversarial Data Augmentation
This work addresses the challenge of domain generalization for machine learning practitioners, offering an incremental improvement over existing methods.
The paper tackles the problem of learning models that generalize to unseen domains by proposing an adversarial data augmentation method that iteratively adds hard examples from fictitious target domains, resulting in improved performance across unknown target domains on digit recognition and semantic segmentation tasks.
We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-dependent regularization scheme that behaves differently from classical regularizers that regularize towards zero (e.g., ridge or lasso). On digit recognition and semantic segmentation tasks, our method learns models improve performance across a range of a priori unknown target domains.