Wasserstein Distance-Based Domain Adaptation and Its Application to Road Segmentation

Domain adaptation is used in applying a classifier acquired in one data domain to another data domain. A classifier obtained by supervised training with labeled data in an original source domain can also be used for classification in a target domain in which the labeled data are difficult to collect with the help of domain adaptation. The most recently proposed domain adaptation methods focus on data distribution in the feature space of a classifier and bring the data distribution of both domains closer through learning. The present work is based on an existing unsupervised domain adaptation method, in which both distributions become closer through adversarial training between a target data encoder to the feature space and a domain discriminator. We propose to use the Wasserstein distance to measure the distance between two distributions, rather than the well-known Jensen-Shannon divergence. Wasserstein distance, or earth mover's distance, measures the length of the shortest path among all possible pairs between a corresponding pair of variables in two distributions. Therefore, minimization of the distance leads to overlap of the corresponding data pair in source and target domain. Thus, the classifier trained in the source domain becomes also effective in the target domain. The proposed method using Wasserstein distance shows higher accuracies in the target domains compared with an original distance in computer experiments on semantic segmentation of map images.