Revisiting the Parameter Efficiency of Adapters from the Perspective of
  Precision Redundancy
Shibo Jie Haoqing Wang Zhi-Hong Deng
Current state-of-the-art results in computer vision depend in part on
fine-tuning large pre-trained vision models. However, with the exponential
growth of model sizes, the conventional full fine-tuning, which needs to store
a individual network copy for each tasks, leads to increasingly huge storage
and transmission overhead. Adapter-based Parameter-Efficient Tuning (PET)
methods address this challenge by tuning lightweight adapters inserted into the
frozen pre-trained models. In this paper, we investigate how to make adapters
even more efficient, reaching a new minimum size required to store a
task-specific fine-tuned network. Inspired by the observation that the
parameters of adapters converge at flat local minima, we find that adapters are
resistant to noise in parameter space, which means they are also resistant to
low numerical precision. To train low-precision adapters, we propose a
computational-efficient quantization method which minimizes the quantization
error. Through extensive experiments, we find that low-precision adapters
exhibit minimal performance degradation, and even 1-bit precision is sufficient
for adapters. The experimental results demonstrate that 1-bit adapters
outperform all other PET methods on both the VTAB-1K benchmark and few-shot
FGVC tasks, while requiring the smallest storage size. Our findings show, for
the first time, the significant potential of quantization techniques in PET,
providing a general solution to enhance the parameter efficiency of
adapter-based PET methods. Code: https://github.com/JieShibo/PETL-ViT