Few-Shot Domain Adaptation aims to transfer knowledge learned from a known domain to a closely related novel domain with only a few training data available for each class. The limited number of target training data makes it challenging to bridge the domain gap and can easily lead to overfitting. In this paper, we proposed HardMix as a regularization technique which interpolates the data in feature space and assigns augmented features with ‘hard’ labels to eliminate the domain discrepancy. In order to generate a better decision boundary and a more compact intra-class distribution, an adaptive triplet loss is proposed to constrain the ‘hard’ samples near the decision boundary. We demonstrated its effectiveness by comparing our results with the state-of-the-art methods on several benchmark datasets.