FlexAdapt: Flexible cycle-consistent adversarial domain adaptation

Akhil Mathur, A Isopoussu, F Kawsar, Nadia Berthouze, Nic Lane
in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Conference paper (text), Boca Raton, FL, USA

Abstract

© 2019 IEEE. Unsupervised domain adaptation is emerging as a powerful technique to improve the generalizability of deep learning models to new image domains without using any labeled data in the target domain. In the literature, solutions which perform cross-domain feature-matching (e.g., ADDA), pixel-matching (CycleGAN), and combination of the two (e.g., CyCADA) have been proposed for unsupervised domain adaptation. Many of these approaches make a strong assumption that the source and target label spaces are the same, however in the real-world, this assumption does not hold true. In this paper, we propose a novel solution, FlexAdapt, which extends the state-of-the-art unsupervised domain adaptation approach of CyCADA to scenarios where the label spaces in source and target domains are only partially overlapped. Our solution beats a number of state-of-the-art baseline approaches by as much as 29% in some scenarios, and represent a way forward for applying domain adaptation techniques in the real world.