# What Do We Understand About Convolutional Networks?

arXiv: https://arxiv.org/abs/1803.08834 · HTML: https://mlsanity.com/paper/1803.08834/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [TUCaN: Progressively Teaching Colourisation to Capsules](https://mlsanity.com/paper/2106.15176/) (2021)
- [SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis](https://mlsanity.com/paper/2501.03836/) (2025)
- [Collaboration among Image and Object Level Features for Image Colourisation](https://mlsanity.com/paper/2101.07576/) (2021)
- [A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer](https://mlsanity.com/paper/2106.01927/) (2021)
- [UW-ProCCaps: UnderWater Progressive Colourisation with Capsules](https://mlsanity.com/paper/2307.01091/) (2023)
- [Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition](https://mlsanity.com/paper/2309.07187/) (2023)

## Abstract (arXiv)

This document will review the most prominent proposals using multilayer convolutional architectures. Importantly, the various components of a typical convolutional network will be discussed through a review of different approaches that base their design decisions on biological findings and/or sound theoretical bases. In addition, the different attempts at understanding ConvNets via visualizations and empirical studies will be reviewed. The ultimate goal is to shed light on the role of each layer of processing involved in a ConvNet architecture, distill what we currently understand about ConvNets and highlight critical open problems.