Cellular Binary Neural Network for Accurate Image Classification and Semantic Segmentation

Abstract

This paper presents the Cellular Binary Neural Network (CBNN), which is an efficient deep neural network with binary weights and activations. To address the challenge of performance drop caused by low-precision representation, the CBNN adopts multiple subnets which are connected via learnable global lateral paths. The introduced lateral connections are assumed to be sparse and grouped with respect to different source layers. The inter-network lateral connections and inner-network parameters are simultaneously optimized by the distributional loss, classification loss and the group sparse regularization term. Experiments on the CIFAR-10 and ImageNet datasets showed that, by incorporating optimized group-sparse lateral paths, the CBNN outperformed many state-of-the-art binary neural networks in terms of classification accuracy. Besides, to verify the generalization of the proposed binary model, we extended the CBNN on semantic segmentation task. CBNN takes advantage of the multiple subnets to derive the more informative feature maps which are computed by the parallel aggregation in the last convolution block. Experiments on PASCAL VOC segmentation dataset demonstrated that, under the same segmentation settings, the proposed method achieved the superior performance over other compared networks and even the full-precision counterpart.