  Domain adaptation is critical for learning in new and unseen environments.
With domain adversarial training, deep networks can learn disentangled and
transferable features that effectively diminish the dataset shift between the
source and target domains for knowledge transfer. In the era of Big Data, the
ready availability of large-scale labeled datasets has stimulated wide interest
in partial domain adaptation (PDA), which transfers a recognizer from a labeled
large domain to an unlabeled small domain. It extends standard domain
adaptation to the scenario where target labels are only a subset of source
labels. Under the condition that target labels are unknown, the key challenge
of PDA is how to transfer relevant examples in the shared classes to promote
positive transfer, and ignore irrelevant ones in the specific classes to
mitigate negative transfer. In this work, we propose a unified approach to PDA,
Example Transfer Network (ETN), which jointly learns domain-invariant
representations across the source and target domains, and a progressive
weighting scheme that quantifies the transferability of source examples while
controlling their importance to the learning task in the target domain. A
thorough evaluation on several benchmark datasets shows that our approach
achieves state-of-the-art results for partial domain adaptation tasks.
