Generalization with deep learning : for improvement on sensing capability /
edited by Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore.
- 1 online resource (xii, 314 pages)
Includes bibliographical references and index.
Introduction of deep learning algorithms. An introduction of deep learning methods for sensing applications / Keyu Wu, Wei Cui, Vuong Nhu Khue and Efe Camci -- Deep learning for activity sensing. Hierarchically aggregated deep convolutional neural networks for action recognition / Le Zhang, Jagannadan Varadarajan, Yong Pei and Zhenghua Chen . Combining domain knowledge and deep learning to improve HAR models / Massinissa Hamidi and Aomar Osmani . Deep learning and unsupervised domain adaptation for WiFi-based sensing / Jianfei Yang, Han Zou, Lihua Xie and Costas J Spanos . Deep learning for device-free human activity recognition using WiFi signals / Linlin Guo, Hang Zhang, Weiyu Guo, Jian Fang, Bingxian Lu, Chenfei Ma, Guanglin Li, Chuang Lin and Lei Wang . Graph convolutional neural network for skeleton-based video abnormal behavior detection / Weixin Luo, Wen Liu and Shenghua Gao -- deep learning for remote sensing. Perspective on deep learning for earth sciences / Gustau Camps-Valls . Accurate detection of built-up areas in remote sensing image via deep learning / Yihua Tan, Shengzhou Xiong and Pei Yan . Recent advances of manifold-based graph convolutional networks for remote sensing images recognition / Sichao Fu and Weifeng Liu -- Deep learning for medical sensing. Deep retinal image non-uniform illumination removal / Chongyi Li, Huazhu Fu, Miao Yang, Runmin Cong and Chunle Guo . A comparative analysis of efficient CNN-based brain tumor classification models / Tanveer Hussain, Amin Ullah, Umair Haroon, Khan Muhammad and Sung Wook Baik . Classification of travel patterns including wandering based on bi-directional long short-term memory networks / Nhu Khue Vuong, Yong Liu, Syin Chan, Chiew Tong Lau, Zhenghua Chen, Min Wu and Xiaoli Li.
"Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"--Publisher's website.
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