# Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks

arXiv: https://arxiv.org/abs/1807.11720 · HTML: https://mlsanity.com/paper/1807.11720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

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

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation](https://mlsanity.com/paper/2411.01710/) (2024)
- [The Unheard Alternative: Contrastive Explanations for Speech-to-Text Models](https://mlsanity.com/paper/2509.26543/) (2025)
- [Feature CAM: Interpretable AI in Image Classification](https://mlsanity.com/paper/2403.05658/) (2024)

## Abstract (arXiv)

Recently, many methods to interpret and visualize deep neural network predictions have been proposed and significant progress has been made. However, a more class-discriminative and visually pleasing explanation is required. Thus, this paper proposes a region-based approach that estimates feature importance in terms of appropriately segmented regions. By fusing the saliency maps generated from multi-scale segmentations, a more class-discriminative and visually pleasing map is obtained. We incorporate this regional multi-scale concept into a prediction difference method that is model-agnostic. An input image is segmented in several scales using the super-pixel method, and exclusion of a region is simulated by sampling a normal distribution constructed using the boundary prior. The experimental results demonstrate that the regional multi-scale method produces much more class-discriminative and visually pleasing saliency maps.