Summary of Forward Learning For Gradient-based Black-box Saliency Map Generation, by Zeliang Zhang et al.
Forward Learning for Gradient-based Black-box Saliency Map Generation
by Zeliang Zhang, Mingqian Feng, Jinyang Jiang, Rongyi Zhu, Yijie Peng, Chenliang Xu
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel framework for estimating gradients in black-box deep learning models, such as ChatGPT. Traditional gradient-based saliency maps are challenging to compute in these settings due to the complexity of the models. The authors employ the likelihood ratio method to estimate output-to-input gradients and utilize them for generating saliency maps. Additionally, they propose blockwise computation techniques to enhance estimation accuracy. The paper demonstrates the effectiveness of their method through extensive experiments in black-box settings, showcasing accurate gradient estimation and explainability of generated saliency maps. Furthermore, it showcases the scalability of the approach by applying it to GPT-Vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper makes a new way to understand how big artificial intelligence (AI) models make decisions. These AI models are like super powerful computers that can do lots of things on their own, but sometimes we need to know why they made certain choices. The problem is that these models are very hard to understand because they’re so complex. This paper introduces a new way to figure out what the models are thinking by looking at how they change when you give them different information. They tested this method and found it works well, even for really big models like GPT-Vision. |
Keywords
* Artificial intelligence * Deep learning * Gpt * Likelihood
