Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets.

该研究论文介绍了一种名为 Selective Adversarial Network (SAN) 的选择性对抗网络，用于解决部分转移学习（partial transfer learning）问题，从而实现大数据时代的分类和特征转移学习优化。实验结果显示，我们的模型优于多个基准数据集上的现有最先进结果。