  Recently, remarkable progress has been made in learning transferable
representation across domains. Previous works in domain adaptation are majorly
based on two techniques: domain-adversarial learning and self-training.
However, domain-adversarial learning only aligns feature distributions between
domains but does not consider whether the target features are discriminative.
On the other hand, self-training utilizes the model predictions to enhance the
discrimination of target features, but it is unable to explicitly align domain
distributions. In order to combine the strengths of these two methods, we
propose a novel method called Adversarial-Learned Loss for Domain Adaptation
(ALDA). We first analyze the pseudo-label method, a typical self-training
method. Nevertheless, there is a gap between pseudo-labels and the ground
truth, which can cause incorrect training. Thus we introduce the confusion
matrix, which is learned through an adversarial manner in ALDA, to reduce the
gap and align the feature distributions. Finally, a new loss function is
auto-constructed from the learned confusion matrix, which serves as the loss
for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches
in four standard domain adaptation datasets. Our code is available at
https://github.com/ZJULearning/ALDA.
