  The waive of labels in the target domain makes Unsupervised Domain Adaptation
(UDA) an attractive technique in many real-world applications, though it also
brings great challenges as model adaptation becomes harder without labeled
target data. In this paper, we address this issue by seeking compensation from
target domain prior knowledge, which is often (partially) available in
practice, e.g., from human expertise. This leads to a novel yet practical
setting where in addition to the training data, some prior knowledge about the
target class distribution are available. We term the setting as
Knowledge-guided Unsupervised Domain Adaptation (KUDA). In particular, we
consider two specific types of prior knowledge about the class distribution in
the target domain: Unary Bound that describes the lower and upper bounds of
individual class probabilities, and Binary Relationship that describes the
relations between two class probabilities. We propose a general rectification
module that uses such prior knowledge to refine model generated pseudo labels.
The module is formulated as a Zero-One Programming problem derived from the
prior knowledge and a smooth regularizer. It can be easily plugged into
self-training based UDA methods, and we combine it with two state-of-the-art
methods, SHOT and DINE. Empirical results on four benchmarks confirm that the
rectification module clearly improves the quality of pseudo labels, which in
turn benefits the self-training stage. With the guidance from prior knowledge,
the performances of both methods are substantially boosted. We expect our work
to inspire further investigations in integrating prior knowledge in UDA. Code
is available at https://github.com/tsun/KUDA.
