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
Recommended Citation
GB/T 7714
Wang, Peisong,Cheng, Jian. Fixed-point Factorized Networks[C],2017:4012-4020.
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