mlsanityData to 7 Oct 2026

Bridging Interpretability and Robustness Using LIME-Guided Model Refinement

arXiv 2412.18952 ↗First posted 25 Dec 2024cs.LG cs.AIcited by 8

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Abstract (arXiv)

This paper explores the intricate relationship between interpretability and robustness in deep learning models. Despite their remarkable performance across various tasks, deep learning models often exhibit critical vulnerabilities, including susceptibility to adversarial attacks, over-reliance on spurious correlations, and a lack of transparency in their decision-making processes. To address these limitations, we propose a novel framework that leverages Local Interpretable Model-Agnostic Explanations (LIME) to systematically enhance model robustness. By identifying and mitigating the influence of irrelevant or misleading features, our approach iteratively refines the model, penalizing reliance on these features during training. Empirical evaluations on multiple benchmark datasets demonstrate that LIME-guided refinement not only improves interpretability but also significantly enhances resistance to adversarial perturbations and generalization to out-of-distribution data.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 25 Dec 2024.

Builds on

14 of 28 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  7. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  8. 2019Invariant Risk Minimizationcited by 1,832
  9. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  10. 2018Sanity Checks for Saliency Mapscited by 1,149
  11. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  12. 2017Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradientscited by 236
2 more
  1. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  2. 2024Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustnesscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustnesscited by 8
  2. 2025Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustnesscited by 4
  3. 2026Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attackscited by 3
  4. 2026Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysiscited by 3
  5. 2026Learning to Select Like Humans: Explainable Active Learning for Medical Imagingcited by 2
  6. 2026Explainable Novel Category Discovery in Semantic Concept Spacecited by 1
  7. 2026Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphscited by 0
  8. 2026Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AIcited by 0