SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models

Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino

Abstract: Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

External IDs:dblp:conf/cvpr/ChenLWWBWK22