Institutional Repository of Radio Astronomy Research Laboratory
| Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution | |
Xu, Wendi1,3 ; Zhang, Ming1,2
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| 2019-04-15 | |
| Conference Name | 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018 |
| Source Publication | 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018 |
| Pages | 910-914 |
| Conference Date | November 23, 2018 - November 25, 2018 |
| Conference Place | Nanjing, China |
| Country | China |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Contribution Rank | 1 |
| Abstract | Evolution of deep learning shows that some algorithmic tricks are more durable, while others are not. To the best of our knowledge, we firstly summarize 5 more durable and complete deep learning components for vision, that is, WARSHIP. Moreover, we give a biological overview of WARSHIP, emphasizing brain-inspired computing of WARSHIP. As a step towards WARSHIP, our case study of image super resolution combines 3 components of RSH to deploy a CNN model of WARSHIP-XZNet, which performs a happy medium between speed and performance. 2018 IEEE. |
| Keyword | Warships Brain Cloud computing Deep learning Image processing Neural networks Optical resolving power |
| DOI | 10.1109/CCIS.2018.8691350 |
| URL | 查看原文 |
| Indexed By | EI |
| Language | 英语 |
| ISBN | 9781538660041 |
| EI Accession Number | 20191806864525 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/3919 |
| Collection | 射电天文研究室_星系宇宙学研究团组 |
| Affiliation | 1.Xinjiang Astronomical Observatories, Chinese Academy of Sciences, Urumqi, 830011, China 2.Key Laboratory for Radio Astronomy, Chinese Academy of Sciences, Nanjing, 210008, China; 3.University of Chinese Academy of Sciences, Beijing, 100876, China |
| Recommended Citation GB/T 7714 | Xu, Wendi,Zhang, Ming. Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution[C]:Institute of Electrical and Electronics Engineers Inc.,2019:910-914. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Xu-2019-Towards WARS（752KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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