  Existing methods based on meta-learning predict novel-class labels for
(target domain) testing tasks via meta knowledge learned from (source domain)
training tasks of base classes. However, most existing works may fail to
generalize to novel classes due to the probably large domain discrepancy across
domains. To address this issue, we propose a novel adversarial feature
augmentation (AFA) method to bridge the domain gap in few-shot learning. The
feature augmentation is designed to simulate distribution variations by
maximizing the domain discrepancy. During adversarial training, the domain
discriminator is learned by distinguishing the augmented features (unseen
domain) from the original ones (seen domain), while the domain discrepancy is
minimized to obtain the optimal feature encoder. The proposed method is a
plug-and-play module that can be easily integrated into existing few-shot
learning methods based on meta-learning. Extensive experiments on nine datasets
demonstrate the superiority of our method for cross-domain few-shot
classification compared with the state of the art. Code is available at
https://github.com/youthhoo/AFA_For_Few_shot_learning
