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Author (up) Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer edit  openurl
Title Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation Type Conference Article
Year 2022 Publication 36th Conference on Neural Information Processing Systems Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition
that local neighbors in feature space should have more similar predictions than
other features, we propose to optimize an objective of prediction consistency. This
objective encourages local neighborhood features in feature space to have similar
predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method.
 
Address Virtual; November 2022  
Corporate Author Thesis  
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Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
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Area Expedition Conference NEURIPS  
Notes LAMP; 600.147;CIC Approved no  
Call Number Admin @ si @ YWW2022a Serial 3792  
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