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Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
arXiv - CS - Artificial Intelligence Pub Date : 2021-07-28 , DOI: arxiv-2107.13469
Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

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.

中文翻译:

具有条件和标签移位的对抗性无监督域适应:推断、对齐和迭代

在这项工作中,我们提出了一种具有固有条件和标签转换的对抗性无监督域适应 (UDA) 方法,我们的目标是对齐 $p(x|y)$ 和 $p(y)$ 的分布。由于标签在目标域中是不可访问的,传统的对抗性 UDA 假设 $p(y)$ 跨域是不变的,并依赖于对齐 $p(x)$ 作为 $p(x|y)$ 对齐的替代. 为了解决这个问题,我们对条件和标签转换下的传统对抗性 UDA 方法进行了彻底的理论和实证分析,并提出了一种新颖实用的对抗性 UDA 替代优化方案。具体来说,我们在训练中迭代地推断出边际 $p(y)$ 并对齐 $p(x|y)$,并在测试中精确对齐后验 $p(y|x)$。
更新日期:2021-07-29
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