二值网络的分阶段残差二值化算法

二值网络在速度、能耗、内存占用等方面优势明显,但会对深度网络模型造成较大的精度损失.为了解决上述问题,本文提出了二值网络的分阶段残差二值化优化算法,以得到精度更好的二值神经网络模型.本文将随机量化的方法与XNOR-net相结合,提出了两种改进算法带有近似因子的随机权重二值化和确定权重二值化,以及一种全新的分阶段残差二值化的BNN训练优化算法,以得到接近全精度神经网络的识别准确率.实验表明,本文提出的分阶段残差二值化算法能够有效提升二值模型的训练精度,而且不会增加相关网络在测试过程中的计算量,从而保持了二值网络速度快、空间小、能耗低的优势.

Staged Residual Binarization Algorithm for Binary Networks

Binary networks have obvious advantages in terms of speed, energy consumption, and memory consumption, but they cause a great loss of accuracy for the deep network model. In order to solve the problems above, this study proposes a staged residual binarization optimization algorithm for binary networks to obtain a better binary neural network model. In this study, we combine the random quantification method with XNOR-net, and propose two improved algorithms, namely applying weights approximation factor and deterministic quantization networks, and a new staged residual binarization BNN training optimization algorithm, in order to obtain the recognition accuracy of the full-accuracy neural network. Experimental results show that staged residual binarization algorithm can effectively improve the training accuracy of binary model, and does not increase the computational complexity of the related network in the testing process, thus maintaining the advantages of high speed, low memory usage, and small energy consumption.