Generating unlabeled data has been recently shown to help address the
few-shot hypothesis adaptation (FHA) problem, where we aim to train a
classifier for the target domain with a few labeled target-domain data and a
well-trained source-domain classifier (i.e., a source hypothesis), for the
additional information of the highly-compatible unlabeled data. However, the
generated data of the existing methods are extremely similar or even the same.
The strong dependency among the generated data will lead the learning to fail.
In this paper, we propose a diversity-enhancing generative network (DEG-Net)
for the FHA problem, which can generate diverse unlabeled data with the help of
a kernel independence measure: the Hilbert-Schmidt independence criterion
(HSIC). Specifically, DEG-Net will generate data via minimizing the HSIC value
(i.e., maximizing the independence) among the semantic features of the
generated data. By DEG-Net, the generated unlabeled data are more diverse and
more effective for addressing the FHA problem. Experimental results show that
the DEG-Net outperforms existing FHA baselines and further verifies that
generating diverse data plays a vital role in addressing the FHA problem

本文提出一种增强多样性的生成网络 (DEG-Net)，用于 few-shot hypothesis adaptation 问题，通过最小化语义特征之间的 Hilbert-Schmidt 独立性来生成多样化的无标注数据，得到的结果比现有方法更有效。