Summary of When Does Visual Prompting Outperform Linear Probing For Vision-language Models? a Likelihood Perspective, by Hsi-ai Tsao et al.
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
by Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Tsung-Yi Ho
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed log-likelihood ratio (LLR) approach evaluates the comparative benefits of visual prompting and linear probing, two state-of-the-art transfer learning methods. By leveraging LLR scores alongside resource-efficient visual prompts approximations, the method achieves up to a 100-fold reduction in run time compared to full training while maintaining prediction accuracies of up to 91%. This paper explores the effectiveness of these approaches on various datasets and provides insights into their comparative benefits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to adapt pre-trained models to new tasks is being explored. By using special techniques like visual prompting or linear probing, researchers can improve how well a model performs on a new task. One issue with this approach is that it can be very time-consuming. A team of scientists has developed a new method called log-likelihood ratio (LLR) that makes it faster and more efficient. This method can reduce the time it takes to train a model by as much as 100 times while still achieving good results. |
Keywords
» Artificial intelligence » Log likelihood » Prompting » Transfer learning
