Sparsity-Inducing Binarized Neural Networks


  • Peisong Wang Chinese Academy of Sciences
  • Xiangyu He Chinese Academy of Sciences
  • Gang Li Chinese Academy of Sciences
  • Tianli Zhao Chinese Academy of Sciences
  • Jian Cheng Chinese Academy of Sciences



Binarization of feature representation is critical for Binarized Neural Networks (BNNs). Currently, sign function is the commonly used method for feature binarization. Although it works well on small datasets, the performance on ImageNet remains unsatisfied. Previous methods mainly focus on minimizing quantization error, improving the training strategies and decomposing each convolution layer into several binary convolution modules. However, whether sign is the only option for binarization has been largely overlooked. In this work, we propose the Sparsity-inducing Binarized Neural Network (Si-BNN), to quantize the activations to be either 0 or +1, which introduces sparsity into binary representation. We further introduce trainable thresholds into the backward function of binarization to guide the gradient propagation. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and BNNs on mainstream architectures, achieving the new state-of-the-art on binarized AlexNet (Top-1 50.5%), ResNet-18 (Top-1 59.7%), and VGG-Net (Top-1 63.2%). At inference time, Si-BNN still enjoys the high efficiency of exclusive-not-or (xnor) operations.




How to Cite

Wang, P., He, X., Li, G., Zhao, T., & Cheng, J. (2020). Sparsity-Inducing Binarized Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12192-12199.



AAAI Technical Track: Vision