# Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

Source: [Visualizing Deep Neural Network Decisions: Prediction Difference Analysis](<https://www.sota2.com/research/paper/68fa63201e209131ce9fd0d8>)

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

Data updated: 2025-10-25T20:34:16.778Z

Authors: Luisa M Zintgraf, Taco S Cohen, Tameem Adel, Max Welling

Published: 2017-02-15T13:25:26.000Z

## Abstract

This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans).

## 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/1702.04595>)

[PDF](<https://arxiv.org/pdf/1702.04595.pdf>)