  Few-shot classification aims to recognize unseen classes with few labeled
samples from each class. Many meta-learning models for few-shot classification
elaborately design various task-shared inductive bias (meta-knowledge) to solve
such tasks, and achieve impressive performance. However, when there exists the
domain shift between the training tasks and the test tasks, the obtained
inductive bias fails to generalize across domains, which degrades the
performance of the meta-learning models. In this work, we aim to improve the
robustness of the inductive bias through task augmentation. Concretely, we
consider the worst-case problem around the source task distribution, and
propose the adversarial task augmentation method which can generate the
inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple
plug-and-play module for various meta-learning models, and improve their
cross-domain generalization capability. We conduct extensive experiments under
the cross-domain setting, using nine few-shot classification datasets:
mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and
ChestX. Experimental results show that our method can effectively improve the
few-shot classification performance of the meta-learning models under domain
shift, and outperforms the existing works.
