Titlebook: Unsupervised Domain Adaptation; Recent Advances and Jingjing Li,Lei Zhu,Zhekai Du Book 2024 The Editor(s) (if applicable) and The Author(s

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Criterion Optimization-Based Unsupervised Domain Adaptation,roduce a method called joint causality-invariant feature learning (JCFL) which leverages a Hilbert-Schmidt independence criterion to identify causal factors. Extensive experiments demonstrate that JCFL consistently improves state-of-the-art methods.

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Continual Test-Time Unsupervised Domain Adaptation,. Finally, to reduce pseudo-label noise, we propose a soft ensemble negative learning mechanism to guide the model optimization using ensemble complementary pseudo-labels. Our method achieves state-of-the-art performance on three widely used continual TTA datasets, particularly in the strong noise setting that we introduced.

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2730-9908 to approach domain adaptation from novel perspectives, which.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 signific

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Unsupervised Domain Adaptation Techniques,ion in areas like computer vision, natural language processing, robotics, and healthcare. This chapter equips readers with a solid understanding of the landscape of unsupervised domain adaptation and sets the context for the in-depth technical chapters that follow.

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