  The practical Domain Adaptation (DA) tasks, e.g., Partial DA (PDA), open-set
DA, universal DA, and test-time adaptation, have gained increasing attention in
the machine learning community. In this paper, we propose a novel approach,
dubbed Adversarial Reweighting with $\alpha$-Power Maximization (ARPM), for PDA
where the source domain contains private classes absent in target domain. In
ARPM, we propose a novel adversarial reweighting model that adversarially
learns to reweight source domain data to identify source-private class samples
by assigning smaller weights to them, for mitigating potential negative
transfer. Based on the adversarial reweighting, we train the transferable
recognition model on the reweighted source distribution to be able to classify
common class data. To reduce the prediction uncertainty of the recognition
model on the target domain for PDA, we present an $\alpha$-power maximization
mechanism in ARPM, which enriches the family of losses for reducing the
prediction uncertainty for PDA. Extensive experimental results on five PDA
benchmarks, i.e., Office-31, Office-Home, VisDA-2017, ImageNet-Caltech, and
DomainNet, show that our method is superior to recent PDA methods. Ablation
studies also confirm the effectiveness of components in our approach. To
theoretically analyze our method, we deduce an upper bound of target domain
expected error for PDA, which is approximately minimized in our approach. We
further extend ARPM to open-set DA, universal DA, and test time adaptation, and
verify the usefulness through experiments.
