Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?

• URL: http://arxiv.org/abs/2401.12902v1
• Date: Tue, 23 Jan 2024 16:48:18 GMT
• Title: Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
• Authors: Cheng Han, Qifan Wang, Yiming Cui, Wenguan Wang, Lifu Huang, Siyuan Qi, Dongfang Liu
• Abstract summary: Visual Prompt Tuning is a parameter-efficient transfer learning technique. We conduct a comprehensive analysis across 19 distinct datasets and tasks. Our study provides insights into VPT's mechanisms, and offers guidance for its optimal utilization.
• Score: 92.23438255540968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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