# How Do ConvNets Understand Image Intensity?

arXiv: https://arxiv.org/abs/2306.00360 · HTML: https://mlsanity.com/paper/2306.00360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Automatic Photo Orientation Detection with Convolutional Neural Networks](https://mlsanity.com/paper/2305.10319/) (2023)

## Abstract (arXiv)

Convolutional Neural Networks (ConvNets) usually rely on edge/shape information to classify images. Visualization methods developed over the last decade confirm that ConvNets rely on edge information. We investigate situations where the ConvNet needs to rely on image intensity in addition to shape. We show that the ConvNet relies on image intensity information using visualization.