Balanced Binary Neural Networks with Gated Residual
This work addresses the challenge of maintaining accuracy in binary neural networks for efficient deployment in resource-constrained environments, representing an incremental improvement over existing methods.
The paper tackles the problem of accuracy loss in binary neural networks due to biased binarization by proposing a Balanced Binary Neural Networks with Gated Residual (BBG), which uses weight balanced binarization and gated residual techniques to preserve information, achieving superior performance in memory consumption, inference speed, and accuracy on tasks like image classification and detection.
Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Networks with Gated Residual (BBG for short). First, a weight balanced binarization is introduced to maximize information entropy of binary weights, and thus the informative binary weights can capture more information contained in the activations. Second, for binary activations, a gated residual is further appended to compensate their information loss during the forward process, with a slight overhead. Both techniques can be wrapped as a generic network module that supports various network architectures for different tasks including classification and detection. We evaluate our BBG on image classification tasks over CIFAR-10/100 and ImageNet and on detection task over Pascal VOC. The experimental results show that BBG-Net performs remarkably well across various network architectures such as VGG, ResNet and SSD with the superior performance over state-of-the-art methods in terms of memory consumption, inference speed and accuracy.