Summary of Rethinking the Principle Of Gradient Smooth Methods in Model Explanation, by Linjiang Zhou et al.
Rethinking the Principle of Gradient Smooth Methods in Model Explanation
by Linjiang Zhou, Chao Ma, Zepeng Wang, Xiaochuan Shi
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes an adaptive approach to reducing noise in gradient-based model explanation methods, building upon the idea of Gradient Smoothing and SmoothGrad. The key innovation lies in re-understanding the role of the crucial hyper-parameter σ from the perspective of confidence level, allowing for a more effective reduction of noise. The proposed method, AdaptGrad, is shown to significantly outperform baseline methods in comprehensive experiments, demonstrating its potential to enhance interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer models easier to understand by reducing “noise” that can make it hard to see how they work. Right now, there’s a way called SmoothGrad that adds noise to help, but the amount of noise is hard to control. The researchers came up with a new approach called AdaptGrad that makes this process better. They showed that their method works well by testing it on different problems and comparing it to other methods. |
