Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

Autor:	Lee, Chen-Yu, Batra, Tanmay, Baig, Mohammad Haris, Ulbricht, Daniel
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at CVPR 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.04064 Zobrazit plný text záznamu View this record from Arxiv