What Do We Understand About Convolutional Networks?
Not on the map. Why?
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 0; 2025: 1.
6 papers.
- 2021TUCaN: Progressively Teaching Colourisation to Capsulescited by 1
- 2025SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosiscited by 1
- 2021Collaboration among Image and Object Level Features for Image Colourisationcited by 0
- 2021A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performercited by 0
- 2023UW-ProCCaps: UnderWater Progressive Colourisation with Capsulescited by 0
- 2023Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decompositioncited by 0
- 2025SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis7 Jan 2025
- 2023Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition13 Sep 2023
- 2023UW-ProCCaps: UnderWater Progressive Colourisation with Capsules3 Jul 2023
- 2021TUCaN: Progressively Teaching Colourisation to Capsules29 Jun 2021
- 2021A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer3 Jun 2021
- 2021Collaboration among Image and Object Level Features for Image Colourisation19 Jan 2021