  Few-shot learning is a challenging task since only few instances are given
for recognizing an unseen class. One way to alleviate this problem is to
acquire a strong inductive bias via meta-learning on similar tasks. In this
paper, we show that such inductive bias can be learned from a flat collection
of unlabeled images, and instantiated as transferable representations among
seen and unseen classes. Specifically, we propose a novel part-based
self-supervised representation learning scheme to learn transferable
representations by maximizing the similarity of an image to its discriminative
part. To mitigate the overfitting in few-shot classification caused by data
scarcity, we further propose a part augmentation strategy by retrieving extra
images from a base dataset. We conduct systematic studies on miniImageNet and
tieredImageNet benchmarks. Remarkably, our method yields impressive results,
outperforming the previous best unsupervised methods by 7.74% and 9.24% under
5-way 1-shot and 5-way 5-shot settings, which are comparable with
state-of-the-art supervised methods.
