Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket

• URL: http://arxiv.org/abs/2211.12933v1
• Date: Wed, 23 Nov 2022 13:08:58 GMT
• Title: Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket
• Authors: Nianhui Guo, Joseph Bethge, Christoph Meinel, Haojin Yang
• Abstract summary: We focus on a problem: how can a binary neural network achieve the crucial accuracy level (e.g., 80%) on ILSVRC-2012 ImageNet? We design a novel binary architecture BNext based on a comprehensive study of binary architectures and their optimization process. We propose a novel knowledge-distillation technique to alleviate the counter-intuitive overfitting problem observed when attempting to train extremely accurate binary models.
• Score: 10.552465253379134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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