  Domain adaptation (DA) is a representation learning methodology that
transfers knowledge from a label-sufficient source domain to a label-scarce
target domain. While most of early methods are focused on unsupervised DA
(UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In
SSDA, a small number of labeled target images are given for training, and the
effectiveness of those data is demonstrated by the previous studies. However,
the previous SSDA approaches solely adopt those data for embedding ordinary
supervised losses, overlooking the potential usefulness of the few yet
informative clues. Based on this observation, in this paper, we propose a novel
method that further exploits the labeled target images for SSDA. Specifically,
we utilize labeled target images to selectively generate pseudo labels for
unlabeled target images. In addition, based on the observation that pseudo
labels are inevitably noisy, we apply a label noise-robust learning scheme,
which progressively updates the network and the set of pseudo labels by turns.
Extensive experimental results show that our proposed method outperforms other
previous state-of-the-art SSDA methods.
