# A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

arXiv: https://arxiv.org/abs/1601.06615 · HTML: https://mlsanity.com/paper/1601.06615/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 3 of 3)

- [Deep Ensemble for Rotorcraft Attitude Prediction](https://mlsanity.com/paper/2306.17104/) (2023)
- [LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection](https://mlsanity.com/paper/2411.11826/) (2024)
- [Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building](https://mlsanity.com/paper/2212.14730/) (2022)

## Abstract (arXiv)

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.