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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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Bibliografische gegevens
Hoofdauteurs: Li, Jingjing (Auteur), Zhu, Lei (Auteur), Du, Zhekai (Auteur)
Coauteur: SpringerLink (Online service)
Formaat: Elektronisch E-boek
Taal:Engels
Gepubliceerd in: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Editie:1st ed. 2024.
Reeks:Machine Learning: Foundations, Methodologies, and Applications,
Onderwerpen:
Machine learning.
Artificial intelligence > Data processing.
Data mining.
Distribution (Probability theory).
Machine Learning.
Data Science.
Data Mining and Knowledge Discovery.
Distribution Theory.
Online toegang:https://doi.org/10.1007/978-981-97-1025-6
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Inhoudsopgave:
  • 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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