  In this paper, we propose several ideas for enhancing a binary network to
close its accuracy gap from real-valued networks without incurring any
additional computational cost. We first construct a baseline network by
modifying and binarizing a compact real-valued network with parameter-free
shortcuts, bypassing all the intermediate convolutional layers including the
downsampling layers. This baseline network strikes a good trade-off between
accuracy and efficiency, achieving superior performance than most of existing
binary networks at approximately half of the computational cost. Through
extensive experiments and analysis, we observed that the performance of binary
networks is sensitive to activation distribution variations. Based on this
important observation, we propose to generalize the traditional Sign and PReLU
functions, denoted as RSign and RPReLU for the respective generalized
functions, to enable explicit learning of the distribution reshape and shift at
near-zero extra cost. Lastly, we adopt a distributional loss to further enforce
the binary network to learn similar output distributions as those of a
real-valued network. We show that after incorporating all these ideas, the
proposed ReActNet outperforms all the state-of-the-arts by a large margin.
Specifically, it outperforms Real-to-Binary Net and MeliusNet29 by 4.0% and
3.6% respectively for the top-1 accuracy and also reduces the gap to its
real-valued counterpart to within 3.0% top-1 accuracy on ImageNet dataset. Code
and models are available at: https://github.com/liuzechun/ReActNet.
