CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Fixed-point Factorized Networks
Wang, Peisong1,2; Cheng, Jian1,2,3
2017
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Pages4012-4020
Conference Date2017.7.21-7.26
Conference PlaceHawaii,USA
Abstract

In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) for pretrained models to reduce the computational complexity as well as the storage requirement of networks. The resulting networks have only weights of -1, 0 and 1, which significantly eliminates the most resource-consuming multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification task show the proposed FFN only requires one-thousandth of multiply operations with comparable accuracy.


KeywordConvolutional Neural Networks Ternary Quantization Network Acceleration Network Compression
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20114
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Peisong,Cheng, Jian. Fixed-point Factorized Networks[C],2017:4012-4020.
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