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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Book 2024based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents act

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Active Learning for Unsupervised Domain Adaptation,roduces two novel techniques to address key limitations of existing active domain adaptation (ADA) methods: estimating target representativeness without source data access and probabilistic uncertainty estimation. First, an energy-based criterion is proposed for selecting representative target sampl

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Continual Test-Time Unsupervised Domain Adaptation,main data during inference with a continuously changing data distribution. Previous methods have been found to lack noise robustness, leading to a significant increase in errors under strong noise. In this chapter, we address the noise robustness problem in continual TTA by offering three effective