  Binary neural networks are the extreme case of network quantization, which
has long been thought of as a potential edge machine learning solution.
However, the significant accuracy gap to the full-precision counterparts
restricts their creative potential for mobile applications. In this work, we
revisit the potential of binary neural networks and focus on a compelling but
unanswered problem: how can a binary neural network achieve the crucial
accuracy level (e.g., 80%) on ILSVRC-2012 ImageNet? We achieve this goal by
enhancing the optimization process from three complementary perspectives: (1)
We design a novel binary architecture BNext based on a comprehensive study of
binary architectures and their optimization process. (2) We propose a novel
knowledge-distillation technique to alleviate the counter-intuitive overfitting
problem observed when attempting to train extremely accurate binary models. (3)
We analyze the data augmentation pipeline for binary networks and modernize it
with up-to-date techniques from full-precision models. The evaluation results
on ImageNet show that BNext, for the first time, pushes the binary model
accuracy boundary to 80.57% and significantly outperforms all the existing
binary networks. Code and trained models are available at:
https://github.com/hpi-xnor/BNext.git.
