  Mainstream approaches for unsupervised domain adaptation (UDA) learn
domain-invariant representations to bridge domain gap. More recently,
self-training has been gaining momentum in UDA. Originated from semi-supervised
learning, self-training uses unlabeled data efficiently by training on
pseudo-labels. However, as corroborated in this work, under distributional
shift in UDA, the pseudo-labels can be unreliable in terms of their large
discrepancy from the ground truth labels. Thereby, we propose Cycle
Self-Training (CST), a principled self-training algorithm that enforces
pseudo-labels to generalize across domains. In the forward step, CST generates
target pseudo-labels with a source-trained classifier. In the reverse step, CST
trains a target classifier using target pseudo-labels, and then updates the
shared representations to make the target classifier perform well on the source
data. We introduce the Tsallis entropy, a novel regularization to improve the
quality of target pseudo-labels. On quadratic neural networks, we prove that
CST recovers target ground truth, while both invariant feature learning and
vanilla self-training fail. Empirical results indicate that CST significantly
improves over prior state-of-the-arts in standard UDA benchmarks across visual
recognition and sentiment analysis tasks.
