SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels.

Autor: Zhao, Henry Hengyuan, Wang, Pichao, Zhao, Yuyang, Luo, Hao, Wang, Fan, Shou, Mike Zheng
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Zdroj: International Journal of Computer Vision; Mar2024, Vol. 132 Issue 3, p731-749, 19p
Abstrakt: Pre-trained vision transformers have strong representations benefit to various downstream tasks. Recently many parameter-efficient fine-tuning (PEFT) methods have been proposed, and their experiments demonstrate that tuning only 1% extra parameters could surpass full fine-tuning in low-data resource scenarios. However, these methods overlook the task-specific information when fine-tuning diverse downstream tasks. In this paper, we propose a simple yet effective method called "Salient Channel Tuning" (SCT) to leverage the task-specific information by forwarding the model with the task images to select partial channels in a feature map that enables us to tune only 1/8 channels leading to significantly lower parameter costs. Experiments outperform full fine-tuning on 18 out of 19 tasks in the VTAB-1K benchmark by adding only 0.11M parameters of the ViT-B, which is 780 × fewer than its full fine-tuning counterpart. Furthermore, experiments on domain generalization and few-shot learning surpass other PEFT methods with lower parameter costs, demonstrating our proposed tuning technique's strong capability and effectiveness in the low-data regime. The code will be available at https://github.com/zhaohengyuan1/SCT.git [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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