Deep learning for image classification

机译：深度学习进行图像分类

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This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

机译：本文提供了深度学习的概述，并介绍了深度学习的几个子领域，包括卷积神经网络的特定教程。将学习图像特征的传统方法与深度学习技术进行了比较。此外，我们还提供了初步的分类结果，在美国国家标准技术研究院混合数据库（MNIST）上的卷积受限Boltzmann机器的基本实现，并说明了如何使用深度学习网络来帮助我们开发强大的性别分类系统。

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来源
《Ground/air multisensor interoperability, integration, and networking for persistent ISR V》|2014年|90790T.1-90790T.10|共10页
会议地点 Baltimore MD(US)
作者
Ryan McCoppin; Mateen Rizki;
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作者单位

Department of Computer Science and Engr., Wright State University, Dayton, OH USA 45435;

Department of Computer Science and Engr., Wright State University, Dayton, OH USA 45435;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
gender recognition; convolutional restricted Boltzmann machine; electro-optical; deep learning; unsupervised feature learning; MNIST; SWAG;

机译：性别认同；卷积受限玻尔兹曼机电光深度学习无监督特征学习； MNIST；赃物;

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Deep learning for image classification

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