  Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that
tackles few-shot learning across different domains. It aims at transferring
prior knowledge learned on the source dataset to novel target datasets. The
CD-FSL task is especially challenged by the huge domain gap between different
datasets. Critically, such a domain gap actually comes from the changes of
visual styles, and wave-SAN empirically shows that spanning the style
distribution of the source data helps alleviate this issue. However, wave-SAN
simply swaps styles of two images. Such a vanilla operation makes the generated
styles ``real'' and ``easy'', which still fall into the original set of the
source styles. Thus, inspired by vanilla adversarial learning, a novel
model-agnostic meta Style Adversarial training (StyleAdv) method together with
a novel style adversarial attack method is proposed for CD-FSL. Particularly,
our style attack method synthesizes both ``virtual'' and ``hard'' adversarial
styles for model training. This is achieved by perturbing the original style
with the signed style gradients. By continually attacking styles and forcing
the model to recognize these challenging adversarial styles, our model is
gradually robust to the visual styles, thus boosting the generalization ability
for novel target datasets. Besides the typical CNN-based backbone, we also
employ our StyleAdv method on large-scale pretrained vision transformer.
Extensive experiments conducted on eight various target datasets show the
effectiveness of our method. Whether built upon ResNet or ViT, we achieve the
new state of the art for CD-FSL. Code is available at
https://github.com/lovelyqian/StyleAdv-CDFSL.
