Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
This addresses domain shift in clinical applications, enabling adaptation with minimal target data, but it is incremental as it builds on existing UDA techniques.
The paper tackles the problem of few-shot unsupervised domain adaptation (FSUDA) in clinical imaging, where only limited unlabeled target data is available, by proposing a Sensitivity-guided Spectral Adversarial MixUp (SAMix) method that generates target-style images to suppress model sensitivity, resulting in improved generalization on multiple public datasets.
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.