  Unsupervised domain adaptation aims to transfer rich knowledge from the
annotated source domain to the unlabeled target domain with the same label
space. One prevalent solution is the bi-discriminator domain adversarial
network, which strives to identify target domain samples outside the support of
the source domain distribution and enforces their classification to be
consistent on both discriminators. Despite being effective, agnostic accuracy
and overconfident estimation for out-of-distribution samples hinder its further
performance improvement. To address the above challenges, we propose a novel
bi-discriminator domain adversarial neural network with class-level gradient
alignment, i.e. BACG. BACG resorts to gradient signals and second-order
probability estimation for better alignment of domain distributions.
Specifically, for accuracy-awareness, we first design an optimizable nearest
neighbor algorithm to obtain pseudo-labels of samples in the target domain, and
then enforce the backward gradient approximation of the two discriminators at
the class level. Furthermore, following evidential learning theory, we
transform the traditional softmax-based optimization method into a Multinomial
Dirichlet hierarchical model to infer the class probability distribution as
well as samples uncertainty, thereby alleviating misestimation of
out-of-distribution samples and guaranteeing high-quality classes alignment. In
addition, inspired by contrastive learning, we develop a memory bank-based
variant, i.e. Fast-BACG, which can greatly shorten the training process at the
cost of a minor decrease in accuracy. Extensive experiments and detailed
theoretical analysis on four benchmark data sets validate the effectiveness and
robustness of our algorithm.
