Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies
were based on direct adaptation from the source domain to the target domain and
have suffered from large domain discrepancies. In this paper, we propose a UDA
method that effectively handles such large domain discrepancies. We introduce a
fixed ratio-based mixup to augment multiple intermediate domains between the
source and target domain. From the augmented-domains, we train the
source-dominant model and the target-dominant model that have complementary
characteristics. Using our confidence-based learning methodologies, e.g.,
bidirectional matching with high-confidence predictions and self-penalization
using low-confidence predictions, the models can learn from each other or from
its own results. Through our proposed methods, the models gradually transfer
domain knowledge from the source to the target domain. Extensive experiments
demonstrate the superiority of our proposed method on three public benchmarks:
Office-31, Office-Home, and VisDA-2017.

介绍了一种针对领域差异性大的无监督域自适应方法，包括使用固定比例的混合增强多个中间域，训练源主导模型和目标主导模型，使用置信度学习方法逐渐实现领域知识的转移。