In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers. It provides a geometrically meaningful guidance to detect target samples that are far from the support of the source and enables efficient distribution alignment in an end-to-end trainable fashion. In the experiments, we validate the effectiveness and genericness of our method on digit and sign recognition, image classification, semantic segmentation, and object detection.
@article{arxiv.1903.04064,
title = {Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation},
author = {Chen-Yu Lee and Tanmay Batra and Mohammad Haris Baig and Daniel Ulbricht},
journal= {arXiv preprint arXiv:1903.04064},
year = {2019}
}