  Prior computer vision research extensively explores adapting pre-trained
vision transformers (ViT) to downstream tasks. However, the substantial number
of parameters requiring adaptation has led to a focus on Parameter Efficient
Transfer Learning (PETL) as an approach to efficiently adapt large pre-trained
models by training only a subset of parameters, achieving both parameter and
storage efficiency. Although the significantly reduced parameters have shown
promising performance under transfer learning scenarios, the structural
redundancy inherent in the model still leaves room for improvement, which
warrants further investigation. In this paper, we propose Head-level Efficient
Adaptation with Taylor-expansion importance score (HEAT): a simple method that
efficiently fine-tuning ViTs at head levels. In particular, the first-order
Taylor expansion is employed to calculate each head's importance score, termed
Taylor-expansion Importance Score (TIS), indicating its contribution to
specific tasks. Additionally, three strategies for calculating TIS have been
employed to maximize the effectiveness of TIS. These strategies calculate TIS
from different perspectives, reflecting varying contributions of parameters.
Besides ViT, HEAT has also been applied to hierarchical transformers such as
Swin Transformer, demonstrating its versatility across different transformer
architectures. Through extensive experiments, HEAT has demonstrated superior
performance over state-of-the-art PETL methods on the VTAB-1K benchmark.
