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

TL;DR

tCURLoRA introduces a tensor CUR decomposition-based fine-tuning method that efficiently adapts large pre-trained models for medical image segmentation, reducing computational costs while improving performance.

Contribution

The paper presents a novel tensor CUR decomposition approach for parameter-efficient fine-tuning, capturing high-dimensional weight structures more effectively than matrix-based methods.

Findings

Outperforms existing PEFT methods in medical image segmentation

Reduces computational and storage overhead during fine-tuning

Effectively captures high-dimensional weight structures

Abstract

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.