  Despite the growing popularity of deep learning technologies, high memory
requirements and power consumption are essentially limiting their application
in mobile and IoT areas. While binary convolutional networks can alleviate
these problems, the limited bitwidth of weights is often leading to significant
degradation of prediction accuracy. In this paper, we present a method for
training binary networks that maintains a stable predefined level of their
information capacity throughout the training process by applying Shannon
entropy based penalty to convolutional filters. The results of experiments
conducted on SVHN, CIFAR and ImageNet datasets demonstrate that the proposed
approach can statistically significantly improve the accuracy of binary
networks.
