Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Jiajin Zhang Hanqing Chao Amit Dhurandhar Pin-Yu Chen Ali Tajer Yangyang Xu Pingkun Yan
Domain shift is a common problem in clinical applications, where the training
images (source domain) and the test images (target domain) are under different
distributions. Unsupervised Domain Adaptation (UDA) techniques have been
proposed to adapt models trained in the source domain to the target domain.
However, those methods require a large number of images from the target domain
for model training. In this paper, we propose a novel method for Few-Shot
Unsupervised Domain Adaptation (FSUDA), where only a limited number of
unlabeled target domain samples are available for training. To accomplish this
challenging task, first, a spectral sensitivity map is introduced to
characterize the generalization weaknesses of models in the frequency domain.
We then developed a Sensitivity-guided Spectral Adversarial MixUp (SAMix)
method to generate target-style images to effectively suppresses the model
sensitivity, which leads to improved model generalizability in the target
domain. We demonstrated the proposed method and rigorously evaluated its
performance on multiple tasks using several public datasets.