# Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task

arXiv: https://arxiv.org/abs/2008.01584 · HTML: https://mlsanity.com/paper/2008.01584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Surrogate Model-Based Explainability Methods for Point Cloud NNs](https://mlsanity.com/paper/2107.13459/) (2021)
- [Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16](https://mlsanity.com/paper/2206.01862/) (2022)
- [Exploring Adversarial Attacks on Neural Networks: An Explainable Approach](https://mlsanity.com/paper/2303.06032/) (2023)
- [NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks](https://mlsanity.com/paper/2303.06151/) (2023)

## Abstract (arXiv)

In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We find that in general, Grad-CAM seems to outperform LIME on this specific task: it yields more detailed insight from the point of view of core performance and 80% of respondents asked to choose between them when it comes to the trust they inspire in the model choose Grad-CAM. However, we also posit that it is more useful to employ these two methods together, as the insights they yield are complementary.