AresB-Net: accurate residual binarized neural networks using shortcut concatenation and shuffled grouped convolution
HyunJin Kim
Abstract
This article proposes a novel network model to achieve better accurate residual binarized convolutional neural networks (CNNs), denoted as AresB-Net. Even though residual CNNs enhance the classification accuracy of binarized neural networks with increasing feature resolution, the degraded classification accuracy is still the primary concern compared with real-valued residual CNNs. AresB-Net consists of novel basic blocks to amortize the severe error from the binarization, suggesting a well-balanced pyramid structure without downsampling convolution. In each basic block, the shortcut is added to the convolution output and then concatenated, and then the expanded channels are shuffled for the next grouped convolution. In the downsampling when stride >1, our model adopts only the max-pooling layer for generating low-cost shortcut. This structure facilitates the feature reuse from the previous layers, thus alleviating the error from the binarized convolution and increasing the classification accuracy with reduced computational costs and small weight storage requirements. Despite low hardware costs from the binarized computations, the proposed model achieves remarkable classification accuracies on the CIFAR and ImageNet datasets.