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Unsupervised Domain Adaptation Recent Advances and Future Perspectives

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicab...

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Bibliographic Details
Main Authors: Li, Jingjing, Zhu, Lei (Author), Du, Zhekai (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2024, 2024
Edition:1st ed. 2024
Series:Machine Learning: Foundations, Methodologies, and Applications
Subjects:
Artificial Intelligence / Data Processing
Machine Learning
Distribution Theory
Data Mining
Data Mining And Knowledge Discovery
Distribution (probability Theory)
Data Science
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 1. Introduction to Domain Adaptation
  • Chapter 2. Unsupervised Domain Adaptation Techniques
  • Chapter 3. Criterion Optimization-Based Unsupervised Domain
  • Chapter 4. Bi-Classifier Adversarial Learning-Based Unsupervised Domain
  • Chapter 5. Source-Free Unsupervised Domain Adaptation
  • Chapter 6. Active Learning for Unsupervised Domain Adaptation
  • Chapter 7. Continual Test-Time Unsupervised Domain Adaptation
  • Chapter 8. Applications
  • Chapter 9. Research Frontier

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