  Domain adversarial learning aligns the feature distributions across the
source and target domains in a two-player minimax game. Existing domain
adversarial networks generally assume identical label space across different
domains. In the presence of big data, there is strong motivation of
transferring deep models from existing big domains to unknown small domains.
This paper introduces partial domain adaptation as a new domain adaptation
scenario, which relaxes the fully shared label space assumption to that the
source label space subsumes the target label space. Previous methods typically
match the whole source domain to the target domain, which are vulnerable to
negative transfer for the partial domain adaptation problem due to the large
mismatch between label spaces. We present Partial Adversarial Domain Adaptation
(PADA), which simultaneously alleviates negative transfer by down-weighing the
data of outlier source classes for training both source classifier and domain
adversary, and promotes positive transfer by matching the feature distributions
in the shared label space. Experiments show that PADA exceeds state-of-the-art
results for partial domain adaptation tasks on several datasets.
