tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation
This work addresses efficiency issues in fine-tuning for resource-constrained environments, specifically in medical image segmentation, and is incremental as it builds on existing parameter-efficient fine-tuning methods.
The paper tackles the computational and storage challenges of fine-tuning large pre-trained models by proposing tCURLoRA, a parameter-efficient fine-tuning method based on tensor CUR decomposition, which outperforms existing methods in medical image segmentation tasks.
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.