  Previous few-shot learning (FSL) works mostly are limited to natural images
of general concepts and categories. These works assume very high visual
similarity between the source and target classes. In contrast, the recently
proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge
from general nature images of many labeled examples to novel domain-specific
target categories of only a few labeled examples. The key challenge of CD-FSL
lies in the huge data shift between source and target domains, which is
typically in the form of totally different visual styles. This makes it very
nontrivial to directly extend the classical FSL methods to address the CD-FSL
task. To this end, this paper studies the problem of CD-FSL by spanning the
style distributions of the source dataset. Particularly, wavelet transform is
introduced to enable the decomposition of visual representations into
low-frequency components such as shape and style and high-frequency components
e.g., texture. To make our model robust to visual styles, the source images are
augmented by swapping the styles of their low-frequency components with each
other. We propose a novel Style Augmentation (StyleAug) module to implement
this idea. Furthermore, we present a Self-Supervised Learning (SSL) module to
ensure the predictions of style-augmented images are semantically similar to
the unchanged ones. This avoids the potential semantic drift problem in
exchanging the styles. Extensive experiments on two CD-FSL benchmarks show the
effectiveness of our method. Our codes and models will be released.
