Summary of Geometric Remove-and-retrain (goar): Coordinate-invariant Explainable Ai Assessment, by Yong-hyun Park et al.
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
by Yong-Hyun Park, Junghoon Seo, Bomseok Park, Seongsu Lee, Junghyo Jo
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to assessing feature importance in explainable AI is proposed, building upon the widely used Remove-and-Retrain (ROAR) method. However, a critical limitation is identified in current pixel-perturbation strategies, which fail to differentiate between various feature attribution methods, compromising evaluation reliability. To address this challenge, the Geometric Remove-and-Retrain (GOAR) approach is introduced, which exploits geometric perspectives to improve feature importance assessment. Experimental results on both synthetic and real-world datasets demonstrate GOAR’s superiority over traditional pixel-centric metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists are trying to figure out how to better understand what features in artificial intelligence models really matter. They’re using an approach called Remove-and-Retrain (ROAR) but found that it has some major flaws. The new method, called Geometric Remove-and-Retrain (GOAR), looks at things from a different angle and seems to work much better. |
