Diversity-enhancing Generative Network for Few-shot Hypothesis
  Adaptation
Ruijiang Dong Feng Liu Haoang Chi Tongliang Liu Mingming Gong Gang Niu Masashi Sugiyama Bo Han
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