卷积神经网络的最新进展

卷积神经网络的最新进展(中文5900字,英文3200字)
摘要：在过去的几年中，深度学习在视觉识别，语音识别和自然语言处理等各种问题上取得了非常好的效果。在不同类型的深度神经网络中，卷积神经网络得到了广泛的研究。利用注释数据量的快速增长和图形处理器单元优势的显着提高，关于卷积神经网络的研究也迅速兴起，并取得了各种成果。在本文中，我们对卷积神经网络的最新进展进行了深入地研究。我们详细介绍了CNN在层次设计，激活功能，损失函数，正则化，优化和快速计算等方面的改进。此外，我们还介绍了卷积神经网络在计算机视觉，语音和自然语言处理中的各种应用。
关键词：卷积神经网络，深度学习
Recent advances in convolutional neural networks
Jason Kuen, Amir Shahroudy.etc
Abstract：In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

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