Recent advances in convolutional neural networks. (May 2018) - Gu, Jiuxiang; Wang, Zhenhua; Kuen, Jason; Ma, Lianyang; Shahroudy, Amir; Shuai, Bing; Liu, Ting; Wang, Xingxing; Wang, Gang; Cai, Jianfei; Chen, Tsuhan
Highlights: We give an overview of the basic components of CNN. We discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation. We introduce the applications of CNN on various tasks, including image classification, object detection, object tracking, pose estimation, text detection, visual saliency detection, action recognition, scene labeling, speech and natural language processing. We discuss the challenges in CNN and give several future research directions. 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.