A Related Work In this section, we briefly review few-shot learning (FSL) and two domain adaptation settings related

Existing FSL methods can be divided into three categories: (1) Augmenting training data set by prior knowledge. Data augmentation via hand-crafted rules serves as pre-processing in FSL methods. Note that our method belongs to category (1). In the hypothesis transfer learning (HTL), we can only access a well-trained source-domain classifier and small labeled or abundant unlabeled target data. Compared with FHA, HTL still requires at least small target data (e.g., at least We state here two known generalization bounds [5] used in our proof.