Recent breakthroughs in computer vision make use of large deep neural
networks, utilizing the substantial speedup offered by GPUs. For applications
running on limited hardware, however, high precision real-time processing can
still be a challenge. One approach to solving this problem is training networks
with binary or ternary weights, thus removing the need to calculate
multiplications and significantly reducing memory size. In this work, we
introduce LR-nets (Local reparameterization networks), a new method for
training neural networks with discrete weights using stochastic parameters. We
show how a simple modification to the local reparameterization trick,
previously used to train Gaussian distributed weights, enables the training of
discrete weights. Using the proposed training we test both binary and ternary
models on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-art
results on most experiments.

本研究使用 LR-nets（局部重参数网络）的方法，通过对神经网络加入离散权重的简单修改，对于 MNIST、CIFAR-10 和 ImageNet 数据集进行测试，表明用离散权重的二进制和三进制模型在大部分实验中能够取得最先进的结果。