  Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling
efforts and attracts considerable attention. Different from previous methods
focusing on learning domain-invariant feature representations, some recent
methods present generic semi-supervised learning (SSL) techniques and directly
apply them to DA tasks, even achieving competitive performance. One of the most
popular SSL techniques is pseudo-labeling that assigns pseudo labels for each
unlabeled data via the classifier trained by labeled data. However, it ignores
the distribution shift in DA problems and is inevitably biased to source data.
To address this issue, we propose a new pseudo-labeling framework called
Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the
classifier bias by introducing an auxiliary classifier for target data only, to
improve the quality of pseudo labels. Specifically, we employ the memory
mechanism and develop two types of non-parametric classifiers, i.e. the nearest
centroid classifier and neighborhood aggregation, without introducing any
additional network parameters. Despite its simplicity in a pseudo
classification objective, ATDOC with neighborhood aggregation significantly
outperforms domain alignment techniques and prior SSL techniques on a large
variety of DA benchmarks and even scare-labeled SSL tasks.
