文献类型：期刊文献

英文题名：tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation

作者：He, Guanghua[1,2]; Cheng, Wangang[2]; Zhu, Hancan[2]; Cai, Xiaohao[3]; Yu, Gaohang[1]

机构：[1] Department of Mathematics, Hangzhou Dianzi University, Hangzhou, 310018, China; [2] School of Mathematics, Physics and Information, Shaoxing University, Shaoxing, 312000, China; [3] School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250041270)

语种：英文

外文关键词：Contrastive Learning - Deep neural networks - Image segmentation - Learning to rank - Matrix algebra - Medical image processing - Transfer learning

外文摘要：Transfer learning, by leveraging knowledge from pre-trained models, has significantly enhanced the performance of target tasks. However, as deep neural networks scale up, full fine-tuning introduces substantial computational and storage challenges in resource-constrained environments, limiting its widespread adoption. To address this, parameter-efficient fine-tuning (PEFT) methods have been developed to reduce computational complexity and storage requirements by minimizing the number of updated parameters. While matrix decomposition-based PEFT methods, such as LoRA, show promise, they struggle to fully capture the high-dimensional structural characteristics of model weights. In contrast, high-dimensional tensors offer a more natural representation of neural network weights, allowing for a more comprehensive capture of higher-order features and multi-dimensional interactions. In this paper, we propose tCURLoRA, a novel fine-tuning method based on tensor CUR decomposition. By concatenating pre-trained weight matrices into a three-dimensional tensor and applying tensor CUR decomposition, we update only the lower-order tensor components during fine-tuning, effectively reducing computational and storage overhead. Experimental results demonstrate that tCURLoRA outperforms existing PEFT methods in medical image segmentation tasks. ? 2025, CC BY-NC-SA.