PokerNet: Expanding Features Cheaply via Depthwise Convolutions
文献类型：期刊论文
| 作者 | Wei Tang1,2 ; Yan Huang1,2; Liang Wang1,2
|
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:3页码:432-442 |
| 关键词 | Deep learning depthwise convolution lightweight deep model model compression model acceleration |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-021-1288-x |
| 英文摘要 | Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models. However, it takes up most of the overall computational cost (usually more than 90%). This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution. As a result, the Poker module can greatly reduce the computational cost, and meanwhile generate a large number of effective features to guarantee the performance. The proposed module is standardized and can be employed wherever the feature expansion is needed. By varying the stride and the number of channels, different kinds of bottlenecks are designed to plug the proposed Poker module into the network. Thus, a lightweight model can be easily assembled. Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module. And our PokerNet models can reduce the computational cost by 7.1%−15.6%. PokerNet models achieve comparable or even higher recognition accuracy than previous state-of-the-art (SOTA) models on the ImageNet ILSVRC2012 classification dataset. Code is available at https://github.com/diaomin/pokernet. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44292]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing 自动化研究所_智能感知与计算研究中心 |
| 作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
| 推荐引用方式 GB/T 7714 | Wei Tang,Yan Huang,Liang Wang. PokerNet: Expanding Features Cheaply via Depthwise Convolutions[J]. International Journal of Automation and Computing,2021,18(3):432-442. |
| APA | Wei Tang,Yan Huang,&Liang Wang.(2021).PokerNet: Expanding Features Cheaply via Depthwise Convolutions.International Journal of Automation and Computing,18(3),432-442. |
| MLA | Wei Tang,et al."PokerNet: Expanding Features Cheaply via Depthwise Convolutions".International Journal of Automation and Computing 18.3(2021):432-442. |
入库方式： OAI收割
来源：自动化研究所
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