CUT: Pruning Pre-Trained Multi-Task Models into Compact Models for Edge Devices

• URL: http://arxiv.org/abs/2504.09803v1
• Date: Mon, 14 Apr 2025 02:04:48 GMT
• Title: CUT: Pruning Pre-Trained Multi-Task Models into Compact Models for Edge Devices
• Authors: Jingxuan Zhou, Weidong Bao, Ji Wang, Zhengyi Zhong,
• Abstract summary: This paper proposes a pre-trained multi-task model pruning method specifically designed for edge devices.<n>The goal is to utilize existing pre-trained multi-task models to construct a compact multi-task model that meets the needs of edge devices.
• Score: 3.183155931478785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning has garnered widespread attention in the industry due to its efficient data utilization and strong generalization capabilities, making it particularly suitable for providing high-quality intelligent services to users. Edge devices, as the primary platforms directly serving users, play a crucial role in delivering multi-task services. However, current multi-task models are often large, and user task demands are increasingly diverse. Deploying such models directly on edge devices not only increases the burden on these devices but also leads to task redundancy. To address this issue, this paper innovatively proposes a pre-trained multi-task model pruning method specifically designed for edge computing. The goal is to utilize existing pre-trained multi-task models to construct a compact multi-task model that meets the needs of edge devices. The specific implementation steps are as follows: First, decompose the tasks within the pre-trained multi-task model and select tasks based on actual user needs. Next, while retaining the knowledge of the original pre-trained model, evaluate parameter importance and use a parameter fusion method to effectively integrate shared parameters among tasks. Finally, obtain a compact multi-task model suitable for edge devices. To validate the effectiveness of the proposed method, we conducted experiments on three public image datasets. The experimental results fully demonstrate the superiority and efficiency of this method, providing a new solution for multi-task learning on edge devices.

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