Contrastive vicinal space for unsupervised domain adaptation

Jaemin Na, Dongyoon Han, Hyung Jin Chang, Wonjun Hwang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two subspaces through the solution of the minimax problem: contrastive space and consensus space. In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories. The effectiveness of our method is demonstrated on public benchmarks, including Office-31, Office-Home, and VisDA-C, achieving state-of-the-art performances. We further show that our method outperforms the current state-of-the-art methods on PACS, which indicates that our instance-wise approach works well for multi-source domain adaptation as well. Code is available at https://github.com/NaJaeMin92/CoVi.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022
Subtitle of host publication17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIV
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer
Pages92–110
Number of pages19
Edition1
ISBN (Electronic)9783031198304
ISBN (Print)9783031198298
DOIs
Publication statusPublished - 22 Oct 2022
Event17th European Conference on Computer Vision (ECCV 2022) - Tel Aviv, Israel
Duration: 24 Oct 202228 Oct 2022

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13694
ISSN (Print)302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th European Conference on Computer Vision (ECCV 2022)
Abbreviated titleECCV 2022
Country/TerritoryIsrael
CityTel Aviv
Period24/10/2228/10/22

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