Summary of Bridging Interpretability and Robustness Using Lime-guided Model Refinement, by Navid Nayyem et al.
Bridging Interpretability and Robustness Using LIME-Guided Model Refinement
by Navid Nayyem, Abdullah Rakin, Longwei Wang
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 investigates how interpretable deep learning models are, despite their impressive performance across various tasks. It highlights the limitations of current models, including vulnerability to attacks, reliance on spurious correlations, and lack of transparency in decision-making. To address these issues, a novel framework is proposed that leverages Local Interpretable Model-Agnostic Explanations (LIME) to enhance model robustness. The approach iteratively refines the model by identifying and mitigating irrelevant features during training. Experimental results on multiple benchmark datasets show that LIME-guided refinement not only improves interpretability but also enhances resistance to attacks and generalization to out-of-distribution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good deep learning models are at explaining their decisions. Despite being very good at some tasks, these models can be tricked into making mistakes or rely on things that aren’t really important. To make them better, the researchers propose a new way to train models using something called Local Interpretable Model-Agnostic Explanations (LIME). This helps models focus on what’s really important and ignore things that are not. The results show that this approach makes models not only more understandable but also stronger against attacks and better at dealing with new, unexpected situations. |
Keywords
* Artificial intelligence * Deep learning * Generalization
