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Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W tohelp binary training. However, pioneering binary works only use W to accumulategradient updates during backward propagation, which can not fully exploit itspower and may hinder novel advances in BNNs. In this work, we explore the roleof W in training besides acting as a latent variable. Notably, we propose toadd W into the computation graph, making it perform as a real-valued featureextractor to aid the binary training. We make different attempts on how toutilize the real-valued weights and propose a specialized supervision.Visualization experiments qualitatively verify the effectiveness of ourapproach in making it easier to distinguish between different categories.Quantitative experiments show that our approach outperforms currentstate-of-the-arts, further closing the performance gap between floating-pointnetworks and BNNs. Evaluation on ImageNet with ResNet-18 (Top-1 63.4%),ResNet-34 (Top-1 67.0%) achieves new state-of-the-art.

Improving Binary Neural Networks through Fully Utilizing Latent Weights