Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

• URL: http://arxiv.org/abs/2107.13469v1
• Date: Wed, 28 Jul 2021 16:28:01 GMT
• Title: Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
• Authors: Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
• Abstract summary: We propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts. We infer the marginal $p(y)$ and align $p(x|y)$ iteratively in the training, and precisely align the posterior $p(y|x)$ in testing.
• Score: 47.67549731439979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both $p(x|y)$ and $p(y)$. Since the label is inaccessible in the target domain, the conventional adversarial UDA assumes $p(y)$ is invariant across domains, and relies on aligning $p(x)$ as an alternative to the $p(x|y)$ alignment. To address this, we provide a thorough theoretical and empirical analysis of the conventional adversarial UDA methods under both conditional and label shifts, and propose a novel and practical alternative optimization scheme for adversarial UDA. Specifically, we infer the marginal $p(y)$ and align $p(x|y)$ iteratively in the training, and precisely align the posterior $p(y|x)$ in testing. Our experimental results demonstrate its effectiveness on both classification and segmentation UDA, and partial UDA.

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