A Survey on Deep Learning: Algorithms, Techniques, and Applications
A Survey on Deep Learning: Algorithms, Techniques, and Applications
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深度学习综述：算法、技术与应用
DOI：
10.1145/3234150
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发表时间：
2019-01-01
影响因子：
16.6
通讯作者：
Iyengar, S. S.
中科院分区：
文献类型：
--
作者：
Pouyanfar, Samira;Sadiq, Saad;Iyengar, S. S.
机器学习领域正在见证其黄金时代，深度学习逐渐成为该领域的领导者。深度学习使用多层来表示数据的抽象，以构建计算模型。一些关键的深度学习算法，如生成对抗网络、卷积神经网络和模型转移，已经完全改变了我们对信息处理的看法。然而，在这个非常快节奏的领域背后存在着理解的缝隙，因为它以前从未从多范围的角度表示过。缺乏对核心的理解使得这些强大的方法成为黑盒机器，在根本层面上抑制了开发。此外，深度学习一再被视为机器学习中所有绊脚石的银弹，这与事实相去甚远。本文全面回顾了视觉、音频和文本处理、社交网络分析和自然语言处理方面的历史和最新最先进的方法，然后深入分析了深度学习应用中的旋转和突破性进展。它还审查了深度学习面临的问题，如无监督学习，黑箱模型和在线学习，并说明如何将这些挑战转化为未来多产的研究途径。
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and ground breaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.