Increasing Information Entropy of Both Weights and Activations for the Binary Neural Networks

In terms of memory footprint requirement and computing speed, the binary neural networks (BNNs) have great advantages in power-aware deployment applications, such as AIoT edge terminals, wearable and portable devices, etc. However, the networks’ binarization process inevitably brings considerable information losses, and further leads to accuracy deterioration. To tackle these problems, we initiate analyzing from a perspective of the information theory, and manage to improve the networks information capacity. Based on the analyses, our work has two primary contributions: the first is a newly proposed median loss (ML) regularization technique. It improves the binary weights distribution more evenly, and consequently increases the information capacity of BNNs greatly. The second is the batch median of activations (BMA) method. It raises the entropy of activations by subtracting a median value, and simultaneously lowers the quantization error by computing separate scaling factors for the positive and negative activations procedure. Experiment results prove that the proposed methods utilized in ResNet-18 and ResNet-34 individually outperform the Bi-Real baseline by 1.3% and 0.9% Top-1 accuracy on the ImageNet 2012. Proposed ML and BMA for the storage cost and calculation complexity increments are minor and negligible. Additionally, comprehensive experiments also prove that our methods can be applicable and embedded into the present popular BNN networks with accuracy improvement and negligible overhead increment.