Summary of Normensemblexai: Unveiling the Strengths and Weaknesses Of Xai Ensemble Techniques, by Weronika Hryniewska-guzik et al.
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
by Weronika Hryniewska-Guzik, Bartosz Sawicki, Przemysław Biecek
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 presents a comprehensive comparison of explainable artificial intelligence (XAI) ensembling methods. The authors introduce NormEnsembleXAI, a novel approach that combines minimum, maximum, and average functions with normalization techniques to improve interpretability. They also provide insights into the strengths and weaknesses of XAI ensemble methods, as well as a library for practical implementation, promoting the adoption of transparent and interpretable deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make AI more understandable. The authors are trying to figure out which way is best to combine different explanations from AI models. They came up with a new method called NormEnsembleXAI that combines different ideas to make things clearer. They also talked about the good and bad points of this approach, and gave people a library to help them use it. |
Keywords
* Artificial intelligence * Deep learning
