Efficient convolutional networks learning through irregular convolutional kernels
文献类型：期刊论文
| 作者 | Guo, Weiyu1; Ma, Jiabin4 ; Ouyang, Yidong1; Wang, Liang4; Huang, Yongzhen2,3
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2022-06-07 |
| 卷号 | 489页码:167-178 |
| ISSN号 | 0925-2312 |
| 关键词 | Model compression Interpolation Irregular convolutional kernels |
| DOI | 10.1016/j.neucom.2022.02.065 |
| 通讯作者 | Huang, Yongzhen(hyz@watrix.ai) |
| 英文摘要 | As deep neural networks are increasingly used in applications suited for low-power devices, a fundamen-tal dilemma emerges: the trend is to develop models to use the increasing amount of data, resulting in memory-intensive models; however, low-power devices have very limited memory and cannot store large models. Parameters pruning is critical for deep model deployment on low-power devices. Existing efforts mainly focus on designing highly efficient structures or pruning redundant connections in networks. They are typically sensitive to the tasks or rely on dedicated and expensive hashing storage strategies. In this work, we introduce a novel approach to achieve a lightweight model from the perspec-tive of reconstructing the structure of convolution kernels for efficient storage. Our approach transforms a traditional square convolution kernel into line segments, and automatically learns a proper strategy for equipping these line segments to model diverse features. Experimental results show that our approach can significantly reduce the number of parameters (pruned 69% on DenseNet-40) and calculation costs (pruned 59% on DenseNet-40) while maintaining acceptable performance (only lose less than 2% accuracy). (c) 2022 Elsevier B.V. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[62106290] ; Program for Innovation Research in Central University of Finance and Economics |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000782510100014 |
| 资助机构 | National Natural Science Foundation of China ; Program for Innovation Research in Central University of Finance and Economics |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48332]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Huang, Yongzhen |
| 作者单位 | 1.Cent Univ Finance & Econ, Informat Sch, Beijing, Peoples R China 2.Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China 3.Watrix Technol Co Ltd, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Weiyu,Ma, Jiabin,Ouyang, Yidong,et al. Efficient convolutional networks learning through irregular convolutional kernels[J]. NEUROCOMPUTING,2022,489:167-178. |
| APA | Guo, Weiyu,Ma, Jiabin,Ouyang, Yidong,Wang, Liang,&Huang, Yongzhen.(2022).Efficient convolutional networks learning through irregular convolutional kernels.NEUROCOMPUTING,489,167-178. |
| MLA | Guo, Weiyu,et al."Efficient convolutional networks learning through irregular convolutional kernels".NEUROCOMPUTING 489(2022):167-178. |
入库方式： OAI收割
来源：自动化研究所
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