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View all- Xing BYing XWang R(2024)Rectifying self-training with neighborhood consistency and proximity for source-free domain adaptationNeurocomputing10.1016/j.neucom.2024.128425606(128425)Online publication date: Nov-2024
Partial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate label set. In PLL, when all possible candidate labels are as signed to real-world training examples, ...
Recently, unsupervised domain adaptation (UDA) has attracted extensive interest in relieving the greedy requirement of vanilla deep learning for labeled data. It seeks for a solution to adapt the knowledge from a well-labeled training dataset (...
Recently, domain adaptation learning (DAL) has shown surprising performance by utilizing labeled samples from the source (or auxiliary) domain to learn a robust classifier for the target domain of the interest which has a few or even no labeled samples. ...
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