  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.
