  This paper proposes a novel binarized weight network (BT) for a
resource-efficient neural structure. The proposed model estimates a binary
representation of weights by taking into account the approximation error with
an additional term. This model increases representation capacity and stability,
particularly for shallow networks, while the computation load is theoretically
reduced. In addition, a novel regularization term is introduced that is
suitable for all threshold-based binary precision networks. This term penalizes
the trainable parameters that are far from the thresholds at which binary
transitions occur. This step promotes a swift modification for binary-precision
responses at train time. The experimental results are carried out for two sets
of tasks: visual classification and visual inverse problems. Benchmarks for
Cifar10, SVHN, Fashion, ImageNet2012, Set5, Set14, Urban and BSD100 datasets
show that our method outperforms all counterparts with binary precision.
