  Binary convolutional neural networks (BCNNs) provide a potential solution to
reduce the memory requirements and computational costs associated with deep
neural networks (DNNs). However, achieving a trade-off between performance and
computational resources remains a significant challenge. Furthermore, the fully
connected layer of BCNNs has evolved into a significant computational
bottleneck. This is mainly due to the conventional practice of excluding the
input layer and fully connected layer from binarization to prevent a
substantial loss in accuracy. In this paper, we propose a hybrid model named
ReActXGB, where we replace the fully convolutional layer of ReActNet-A with
XGBoost. This modification targets to narrow the performance gap between BCNNs
and real-valued networks while maintaining lower computational costs.
Experimental results on the FashionMNIST benchmark demonstrate that ReActXGB
outperforms ReActNet-A by 1.47% in top-1 accuracy, along with a reduction of
7.14% in floating-point operations (FLOPs) and 1.02% in model size.
