Deep neural networks (DNN) are widely used in many applications. However, their deployment on edge devices has been difficult because they are resource hungry. Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to $1$-bit. We propose an improved binary training method (BNN+), by introducing a regularization function that encourages training weights around binary values. In addition to this, to enhance model performance we add trainable scaling factors to our regularization functions. Furthermore, we use an improved approximation of the derivative of the sign activation function in the backward computation. These additions are based on linear operations that are easily implementable into the binary training framework. We show experimental results on CIFAR-10 obtaining an accuracy of $86.7\%$, on AlexNet and $91.3\%$ with VGG network. On ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of $4\%$ and $2\%$ top-$1$ accuracy respectively.

本文提出了一种新的正则化函数和可训练缩放因子的二进制训练方法，通过改进梯度下降算法中的反向传播计算，在ImageNet数据集上比传统二进制神经网络和XNOR-net有更好的性能表现。

正则化二进制网络训练