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: (blind URL, see appendix).

通过三个互补的角度: 创新设计一个基于全方位研究后的二进制结构BNext、提出一种新的知识蒸馏技术以缓解训练极精确二进制模型时出现的过拟合问题、并使用来自全精度模型的最新技术现代化二进制网络的数据增强管线, 我们让二进制神经网络在 ILSVRC-2012 ImageNet 上达到了至关重要的准确度水平(例如80.57%)，并且明显优于所有现有的二进制网络。

使用二进制神经网络门票在ImageNet上加入高精度俱乐部