# Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

arXiv: https://arxiv.org/abs/1704.04133 · HTML: https://mlsanity.com/paper/1704.04133/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2017.

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

- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [Contrastive Counterfactual Visual Explanations With Overdetermination](https://mlsanity.com/paper/2106.14556/) (2021)
- [i-Align: an interpretable knowledge graph alignment model](https://mlsanity.com/paper/2308.13755/) (2023)
- [Invariance Measures for Neural Networks](https://mlsanity.com/paper/2310.17404/) (2023)

## Abstract (arXiv)

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.