Generalizing to Unseen Domains via Adversarial Data Augmentation

作者：Riccardo Volpi, Hongseok Namkoong, Ozan Şener, John C. Duchi, Vittorio Murino, Silvio Savarese · 发表于：Neural Information Processing Systems · 年份：2018 · 被引用次数：197 · 研究领域：Domain Adaptation and Few-Shot Learning、Adversarial Robustness in Machine Learning、COVID-19 diagnosis using AI

We are concerned with learning models that generalize well to different 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.