# Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI

Source: [Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI](<https://www.sota2.com/research/paper/68fa6e671e209131cea3fc10>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:56:59.119Z

Authors: Erico Tjoa, Hong Jing Khok, Tushar Chouhan, Guan Cuntai

Published: 2021-12-30T12:46:23.000Z

## Abstract

This paper quantifies the quality of heatmap-based eXplainable AI (XAI) methods w.r.t image classification problem. Here, a heatmap is considered desirable if it improves the probability of predicting the correct classes. Different XAI heatmap-based methods are empirically shown to improve classification confidence to different extents depending on the datasets, e.g. Saliency works best on ImageNet and Deconvolution on Chest X-Ray Pneumonia dataset. The novelty includes a new gap distribution that shows a stark difference between correct and wrong predictions. Finally, the generative augmentative explanation is introduced, a method to generate heatmaps capable of improving predictive confidence to a high level.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2201.00009>)

[PDF](<https://arxiv.org/pdf/2201.00009.pdf>)