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

Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jia You, C-C Jay Kuo, Georges El Fakhri, Jonghye Woo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In this work, we propose an adversarial unsupervised domain adaptation (UDA) method under inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both p(x|y) and p(y). Since labels are inaccessible in a target domain, conventional adversarial UDA methods assume that p(y) is invariant across domains and rely 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 at the training stage, and precisely align the posterior p(y|x) at the testing stage. Our experimental results demonstrate its effectiveness on both classification and segmentation UDA and partial UDA.
Original languageEnglish
Title of host publicationProc. of ICCV'2021
Publication statusPublished - Oct 2021
Event2021 IEEE/CVF Int. Conf. on Computer Vision - Montreal, QC, Canada
Duration: 10 Oct 202117 Oct 2021


Conference2021 IEEE/CVF Int. Conf. on Computer Vision
Abbreviated titleICCV


Dive into the research topics of 'Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate'. Together they form a unique fingerprint.

Cite this