BlockQNN: Efficient Block-Wise Neural Network Architecture Generation
文献类型：期刊论文
| 作者 | Zhong, Zhao1 ; Yang, Zichen2; Deng, Boyang2; Yan, Junjie2; Wu, Wei2; Shao, Jing2; Liu, Cheng-Lin3,4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2021-07-01 |
| 卷号 | 43期号:7页码:2314-2328 |
| 关键词 | Computer architecture Task analysis Neural networks Network architecture Graphics processing units Acceleration Indexes Convolutional neural network neural architecture search AutoML reinforcement learning Q-learning |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2020.2969193 |
| 通讯作者 | Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn) |
| 英文摘要 | Convolutional neural networks have gained a remarkable success in computer vision. However, most popular network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained to choose component layers sequentially. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it yields state-of-the-art results in comparison to the hand-crafted networks on image classification, particularly, the best network generated by BlockQNN achieves 2.35 percent top-1 error rate on CIFAR-10. (2) it offers tremendous reduction of the search space in designing networks, spending only 3 days with 32 GPUs. A faster version can yield a comparable result with only 1 GPU in 20 hours. (3) it has strong generalizability in that the network built on CIFAR also performs well on the larger-scale dataset. The best network achieves very competitive accuracy of 82.0 percent top-1 and 96.0 percent top-5 on ImageNet. |
| 资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61633021] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000692540900011 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45738]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Liu, Cheng-Lin |
| 作者单位 | 1.Univ Chinese Acad Sci, Inst Automat, Chinese Acad Sci, NLPR, Beijing 100190, Peoples R China 2.Sensetime Res Inst, SenseTime Grp Ltd, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 4.Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhong, Zhao,Yang, Zichen,Deng, Boyang,et al. BlockQNN: Efficient Block-Wise Neural Network Architecture Generation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(7):2314-2328. |
| APA | Zhong, Zhao.,Yang, Zichen.,Deng, Boyang.,Yan, Junjie.,Wu, Wei.,...&Liu, Cheng-Lin.(2021).BlockQNN: Efficient Block-Wise Neural Network Architecture Generation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(7),2314-2328. |
| MLA | Zhong, Zhao,et al."BlockQNN: Efficient Block-Wise Neural Network Architecture Generation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.7(2021):2314-2328. |
入库方式： OAI收割
来源：自动化研究所
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