Apr 16, 2024 · This method integrates fuzzy C-means clustering and a novel instance-level fuzzy vector into the prompt learning loss function, minimizing the ...
The proposed method adopts a comprehensive framework, integrating the two key components depicted in Figure 2: (1) fuzzy C-means cluster- ing, enhancing prompt ...
Apr 16, 2024 · This method integrates fuzzy C-means clustering and a novel instance-level fuzzy vector into the prompt learning loss function, minimizing the ...
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Jul 2, 2024 · This method integrates fuzzy C-means clustering and a novel instance-level fuzzy vector into the prompt learning loss function, minimizing the ...
Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning. K Shi, J Lu ... CLIP-Enhanced Unsupervised Domain Adaptation with Consistency Regularization.
Jan 7, 2024 · In response to this question, Transfer Learning has emerged, leveraging previously acquired knowledge from the source domain to enhance model ...
The n-dimensional fuzzy geometry is used to propose a metric to measure the similarity between features on one domain. Then, based on this metric, shared fuzzy ...
Jun 13, 2024 · Abstract:Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the ...
Missing: Fuzzy | Show results with:Fuzzy
To address this issue, this paper presents a novel HeUDA model via <italic>n</italic>-dimensional fuzzy geometry and fuzzy equivalence relations, called F-HeUDA ...
Zhen Fang Publications | University of Technology Sydney
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Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning. 1 Jan 2024IEEE Transactions on Fuzzy Systems32(7):4038-4048. Co-authors: Shi K, Lu J, Fang Z