Facing the Elephant in the Room: Visual Prompt Tuning or Full
  Finetuning?
Cheng Han Qifan Wang Yiming Cui Wenguan Wang Lifu Huang Siyuan Qi Dongfang Liu
As the scale of vision models continues to grow, the emergence of Visual
Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has
gained attention due to its superior performance compared to traditional
full-finetuning. However, the conditions favoring VPT (the ``when") and the
underlying rationale (the ``why") remain unclear. In this paper, we conduct a
comprehensive analysis across 19 distinct datasets and tasks. To understand the
``when" aspect, we identify the scenarios where VPT proves favorable by two
dimensions: task objectives and data distributions. We find that VPT is
preferrable when there is 1) a substantial disparity between the original and
the downstream task objectives (e.g., transitioning from classification to
counting), or 2) a similarity in data distributions between the two tasks
(e.g., both involve natural images). In exploring the ``why" dimension, our
results indicate VPT's success cannot be attributed solely to overfitting and
optimization considerations. The unique way VPT preserves original features and
adds parameters appears to be a pivotal factor. Our study provides insights
into VPT's mechanisms, and offers guidance for its optimal utilization.