A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Abstract
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.
Cite
@article{arxiv.1601.06615,
title = {A Taxonomy of Deep Convolutional Neural Nets for Computer Vision},
author = {Suraj Srinivas and Ravi Kiran Sarvadevabhatla and Konda Reddy Mopuri and Nikita Prabhu and Srinivas S S Kruthiventi and R. Venkatesh Babu},
journal= {arXiv preprint arXiv:1601.06615},
year = {2016}
}
Comments
Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm)