• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • AAAI Officers and Committees
    • AAAI Staff
    • Bylaws of AAAI
    • AAAI Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • Award for Artificial Intelligence for the Benefit of Humanity
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • AAAI Resources
    • AAAI Mailing Lists
    • Past AAAI Presidential Addresses
    • Presidential Panel on Long-Term AI Futures
    • Past AAAI Policy Reports
      • A Report to ARPA on Twenty-First Century Intelligent Systems
      • The Role of Intelligent Systems in the National Information Infrastructure
    • AAAI Logos
    • News
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • IAAI
    • ICWSM
    • HCOMP
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AAAI Press
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
  • aaai-icon_ai-magazine-line-yellowAI Magazine
    • Issues and Articles
    • Author Guidelines
    • Editorial Focus
  • MembershipMembership
    • Member Login
    • Developing Country List
    • AAAI Chapter Program

  • Career CenterCareer Center
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 1: AAAI-22 Technical Tracks 1

Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation

February 1, 2023

Download PDF

Authors

Tong Chu

School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China


Yahao Liu

School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China


Jinhong Deng

School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China


Wen Li

School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China


Lixin Duan

School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China


DOI:

10.1609/aaai.v36i1.19925


Abstract:

Source-Free Unsupervised Domain Adaptation(SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original labeled source domain samples. Many existing SFUDA approaches apply the self-training strategy, which involves iteratively selecting confidently predicted target samples as pseudo-labeled samples used to train the model to fit the target domain. However, the self-training strategy may also suffer from sample selection bias and be impacted by the label noise of the pseudo-labeled samples. In this work, we provide a rigorous theoretical analysis on how these two issues affect the model generalization ability when applying the self-training strategy for the SFUDA problem. Based on this theoretical analysis, we then propose a new Denoised Maximum Classifier Discrepancy (D-MCD) method for SFUDA to effectively address these two issues. In particular, we first minimize the distribution mismatch between the selected pseudo-labeled samples and the remaining target domain samples to alleviate the sample selection bias. Moreover, we design a strong-weak self-training paradigm to denoise the selected pseudo-labeled samples, where the strong network is used to select pseudo-labeled samples while the weak network helps the strong network to filter out hard samples to avoid incorrect labels. In this way, we are able to ensure both the quality of the pseudo-labels and the generalization ability of the trained model on the target domain. We achieve state-of-the-art results on three domain adaptation benchmark datasets, which clearly validates the effectiveness of our proposed approach. Full code is available at https://github.com/kkkkkkon/D-MCD.

Topics: AAAI

Primary Sidebar

HOW TO CITE:

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation AAAI 2022, 472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan (2022). Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. 2022. Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. "Proceedings of the AAAI Conference on Artificial Intelligence". 472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. (2022) "Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation", Proceedings of the AAAI Conference on Artificial Intelligence, p.472-480

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan, "Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation", AAAI, p.472-480, 2022.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. "Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. "Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 472-480.

Tong Chu||Yahao Liu||Jinhong Deng||Wen Li||Lixin Duan. Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. AAAI[Internet]. 2022[cited 2023]; 472-480.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT