Composite Binary Decomposition Networks

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the fullprecision counterparts. In this paper, we propose the composite binary decomposition networks (CBDNet), which first compose real-valued tensor of each layer with a limited number of binary tensors, and then decompose some conditioned binary tensors into two low-rank binary tensors, so that the number of parameters and operations are greatly reduced comparing to the original ones. Experiments demonstrate the effectiveness of the proposed method, as CBDNet can approximate image classification network ResNet-18 using 5. 25 bits, VGG-16 using 5. 47 bits, DenseNet-121 using 5. 72 bits, object detection networks SSD300 using 4. 38 bits, and semantic segmentation networks SegNet using 5. 18 bits, all with minor accuracy drops. 1

Authors

• You Qiaoben
• Zheng Wang National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, China Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China
• Jianguo Li
• Yinpeng Dong
• Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing
• Jun Zhu Tsinghua University National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics

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