Binary Neural Networks (BNNs) show great promise for real-world embedded
devices. As one of the critical steps to achieve a powerful BNN, the scale
factor calculation plays an essential role in reducing the performance gap to
their real-valued counterparts. However, existing BNNs neglect the intrinsic
bilinear relationship of real-valued weights and scale factors, resulting in a
sub-optimal model caused by an insufficient training process. To address this
issue, Recurrent Bilinear Optimization is proposed to improve the learning
process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the
back propagation process. Our work is the first attempt to optimize BNNs from
the bilinear perspective. Specifically, we employ a recurrent optimization and
Density-ReLU to sequentially backtrack the sparse real-valued weight filters,
which will be sufficiently trained and reach their performance limits based on
a controllable learning process. We obtain robust RBONNs, which show impressive
performance over state-of-the-art BNNs on various models and datasets.
Particularly, on the task of object detection, RBONNs have great generalization
performance. Our code is open-sourced on this https URL .

为了优化二值神经网络的学习过程，提出了一种从双线性的角度来优化二值神经网络的学习过程（Recorrent Bilinear Optimization），通过回溯稀疏的真实值权重过滤器，RBONNs 在各个模型和数据集上表现出了令人印象深刻的性能，尤其是在目标检测任务方面，表现出了更好的泛化性能。