# LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations

arXiv: https://arxiv.org/abs/2111.08094 · HTML: https://mlsanity.com/paper/2111.08094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Visualizing Deep Neural Network Decisions: Prediction Difference Analysis](https://mlsanity.com/paper/1702.04595/) (2017)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems](https://mlsanity.com/paper/1906.10263/) (2019)
- [ALIME: Autoencoder Based Approach for Local Interpretability](https://mlsanity.com/paper/1909.02437/) (2019)
- [Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph](https://mlsanity.com/paper/1910.01837/) (2019)
- [Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)](https://mlsanity.com/paper/1806.00663/) (2018)
- [A study of data and label shift in the LIME framework](https://mlsanity.com/paper/1910.14421/) (2019)
- [NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks](https://mlsanity.com/paper/1909.04200/) (2019)
- [Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies](https://mlsanity.com/paper/2012.08333/) (2020)
- [A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation](https://mlsanity.com/paper/2002.07434/) (2020)
- [Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections](https://mlsanity.com/paper/1810.02678/) (2018)
- [Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data](https://mlsanity.com/paper/1910.07856/) (2019)

## Built on by (most cited first, 2 of 2)

- [Model Science: getting serious about verification, explanation and control of AI systems](https://mlsanity.com/paper/2508.20040/) (2025)
- [Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches](https://mlsanity.com/paper/2404.19568/) (2024)

## Abstract (arXiv)

The increased interest in deep learning applications, and their hard-to-detect biases result in the need to validate and explain complex models. However, current explanation methods are limited as far as both the explanation of the reasoning process and prediction results are concerned. They usually only show the location in the image that was important for model prediction. The lack of possibility to interact with explanations makes it difficult to verify and understand exactly how the model works. This creates a significant risk when using the model. The risk is compounded by the fact that explanations do not take into account the semantic meaning of the explained objects. To escape from the trap of static and meaningless explanations, we propose a tool and a process called LIMEcraft. LIMEcraft enhances the process of explanation by allowing a user to interactively select semantically consistent areas and thoroughly examine the prediction for the image instance in case of many image features. Experiments on several models show that our tool improves model safety by inspecting model fairness for image pieces that may indicate model bias. The code is available at: http://github.com/MI2DataLab/LIMEcraft