二值卷积神经网络综述

doi:10.7527/S1000-6893.2020.24504

Abstract

Abstract: In recent years, Binary Convolutional Neural Networks (BNNs) have attracted much attention owing to their low storage and high computational efficiency. However, the mismatch between forward and backward quantization results in a huge performance gap between the BNN and the full-precision convolutional neural network, affecting the deployment of the BNN on resource-constrained platforms. Researchers have proposed a series of algorithms and training methods to reduce the performance gap during the binarization process, thereby promoting the application of BNNs to embedded portable devices. This paper makes a comprehensive review of BNNs, mainly from the perspectives of improving network representative capabilities and fully exploring the network training potential. Specifically, improving network representative capabilities includes the design of the binary quantization method and structure design, while fully exploring the network training potential involves loss function design and the training strategy. Finally, we discuss the performance of BNNs in different tasks and hardware platforms, and summarize the challenges in future research.

Key words: binary convolutional neural networks, full-precision convolutional neural networks, binarization, quantization, model compression, lightweight, deep learning

CLC Number:

V19
TP37

DING Wenrui, LIU Chunlei, LI Yue, ZHANG Baochang. Binary convolutional neural network: Review[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 24504-024504.

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Binary convolutional neural network: Review

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