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Recent breakthroughs in computer vision make use of large deep neuralnetworks, utilizing the substantial speedup offered by GPUs. For applicationsrunning on limited hardware, however, high precision real-time processing canstill be a challenge. One approach to solving this problem is training networkswith binary or ternary weights, thus removing the need to calculatemultiplications and significantly reducing memory size. In this work, weintroduce LR-nets (Local reparameterization networks), a new method fortraining neural networks with discrete weights using stochastic parameters. Weshow how a simple modification to the local reparameterization trick,previously used to train Gaussian distributed weights, enables the training ofdiscrete weights. Using the proposed training we test both binary and ternarymodels on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-artresults on most experiments.

Learning Discrete Weights Using the Local Reparameterization Trick