Semi-Supervised Domain Adaptation Using Explicit Class-Wise Matching for Domain-Invariant and Class-Discriminative Feature Learning

Semi-supervised domain adaptation (SSDA) is a promising technique for various applications. It can transfer knowledge learned from a source domain having high-density labeled samples to a target domain having limited labeled samples. Several previous works have attempted to reduce the distribution discrepancy between source domain and target domain by using adversarial-based or entropy-based methods. These works have improved the performance of SSDA. However, there are still lacunae in producing class-wise domain-invariant features, which impair the improvement of the classification accuracy in the target domain. We propose a novel mapping function using explicit class-wise matching that can make a better decision boundary in the embedding space for superior classification accuracy in the target domain. In general, in a target domain with low-density label samples, it is more challenging to create a well-organized distribution for the classification than in a source domain where rich label information is available. In our mapping function, a representative vector of each class in the embedding spaces of the source and target domains is derived and aligned by using class-wise matching. It is observed that the distribution in the embedding space of the source domain can be effectively reproduced in the target domain. Our method achieves outstanding accuracy of classification in the target domain compared with previous works on the Office-31, Office-Home, Visda2017 and DomainNet datasets.