When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective

arXiv:2409.01821v1 Announce Type: cross
Abstract: Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can significantly improve the performance of out-of-distribution tasks. On the other hand, linear probing, a standard transfer learning method, can sometimes become the best approach. We propose a log-likelihood ratio (LLR) approach to analyze the comparative benefits of visual prompting and linear probing. By employing the LLR score alongside resource-efficient visual prompts approximations, our cost-effective measure attains up to a 100-fold reduction in run time compared to full training, while achieving prediction accuracies up to $91%$. The source code is available at $href{https://github.com/IBM/VP-LLR}{texttt{VP-LLR}}$.



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