GOENet: Group Operations Enhanced Binary Neural Network for Efficient Image Classification

There exists an innegligible performance gap between the binary neural networks and their full-precision counterparts, which prevents their deployment on real-world applications. Recently, plenty of researchers strive to solve this problem by incorporating more binary subnets, improving the representational power with acceptable complexity increase. However, current methods make structure design and parallel acceleration sophisticated and non-trivial and may degenerate the representational power due to the isomorphic subnets. Besides, the final feature fusion method is sub-optimal. In this letter, we propose a simple yet effective binary neural network named GOENet enhanced by group operations. The multiple parallel binary subnets use group-wise binary thresholds to improve feature diversity and are merged into the group convolutional layers. The inter-subnet connections are implemented with the parameter-free group shuffle operations, improving the model parallelism and performance. We also propose the group feature summation module as a better fusion method for its efficiency and effectiveness. Experimental results on ImageNet show that our proposed method outperforms the SOTA GroupNet by 1.7% Top-1 accuracy with 0.84× and 0.89× saving in computation and memory complexity.