# Using KL-divergence to focus Deep Visual Explanation

arXiv: https://arxiv.org/abs/1711.06431 · HTML: https://mlsanity.com/paper/1711.06431/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Towards Safe, Explainable, and Regulated Autonomous Driving](https://mlsanity.com/paper/2111.10518/) (2021)
- [Online Deterministic Annealing for Classification and Clustering](https://mlsanity.com/paper/2102.05836/) (2021)

## Abstract (arXiv)

We present a method for explaining the image classification predictions of deep convolution neural networks, by highlighting the pixels in the image which influence the final class prediction. Our method requires the identification of a heuristic method to select parameters hypothesized to be most relevant in this prediction, and here we use Kullback-Leibler divergence to provide this focus. Overall, our approach helps in understanding and interpreting deep network predictions and we hope contributes to a foundation for such understanding of deep learning networks. In this brief paper, our experiments evaluate the performance of two popular networks in this context of interpretability.