Abstract
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 classifi-
cation and representation models from existing large-scale
domains to unknown small-scale 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.