Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation

Sachin Chhabra, Hemanth Venkateswara, Baoxin Li

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

3 Scopus citations

Abstract

Existing domain adaptation literature comprises multiple techniques that align the labeled source and unlabeled target domains at different stages, and predict the target labels. In a source-free domain adaptation setting, the source data is not available for alignment. We present a source-free generative paradigm that captures the relations between the source categories and enforces them onto the unlabeled target data, thereby circumventing the need for source data without introducing any new hyper-parameters. The adaptation is performed through the adversarial alignment of the posterior probabilities of the source and target categories. The proposed approach demonstrates competitive performance against other source-free domain adaptation techniques and can also be used for source-present settings.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4114-4123
Number of pages10
ISBN (Electronic)9781665493468
DOIs
StatePublished - 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, United States
Duration: Jan 3 2023Jan 7 2023

Publication series

NameProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Conference

Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period1/3/231/7/23

Keywords

  • Algorithms: Machine learning architectures
  • and algorithms (including transfer)
  • formulations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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