NeurIPS2024
V-PETL Bench: A Unified Visual Parameter-Efficient Transfer Learning Benchmark
Yi Xin, Siqi Luo, Xuyang Liu, Yuntao Du, Haodi Zhou, Xinyu Cheng, Christina E. Lee, Junlong Du, Haozhe Wang, Mingcai Chen, Ting Liu, Guimin Hu, Zhongwei Wan, Rongchao Zhang, Aoxue Li, Mingyang Yi, Xiaohong Liu
Abstract
Parameter-efficient transfer learning (PETL) methods show promise in adapting a pre-trained model to various downstream tasks while training only a few parameters. In the computer vision (CV) domain, numerous PETL algorithms have been proposed, but their direct employment or comparison remains inconvenient. To address this challenge, we construct a Unified Visual PETL Benchmark (V-PETL Bench) for the CV domain by selecting 30 diverse, challenging