2016/01/25 by Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri +3 · 1 citation
Computer Science · #cs.CV #cs.LG #cs.MM
paper · pdf · doi:10.3389/frobt.2015.00036
published as Frontiers in Robotics and AI 2(36), January 2016 · Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm)
arxiv created 2016/01/25 · arxiv updated 2016/01/26
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.