Training Ternary Neural Networks with Exact Proximal Operator

In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed method features weight ternarization via an exact formula of proximal operator. Our experiments show that our trained TNN are able to preserve the state-of-the-art performance on MNIST and CIFAR10 benchmark datesets.