Batch normalization (BN) is a key facilitator and considered essential for
state-of-the-art binary neural networks (BNN). However, the BN layer is costly
to calculate and is typically implemented with non-binary parameters, leaving a
hurdle for the efficient implementation of BNN training. It also introduces
undesirable dependence between samples within each batch. Inspired by the
latest advance on Batch Normalization Free (BN-Free) training, we extend their
framework to training BNNs, and for the first time demonstrate that BNs can be
completed removed from BNN training and inference regimes. By plugging in and
customizing techniques including adaptive gradient clipping, scale weight
standardization, and specialized bottleneck block, a BN-free BNN is capable of
maintaining competitive accuracy compared to its BN-based counterpart.
Extensive experiments validate the effectiveness of our proposal across diverse
BNN backbones and datasets. For example, after removing BNs from the
state-of-the-art ReActNets, it can still be trained with our proposed
methodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10,
CIFAR-100, and ImageNet respectively, with marginal performance drop
(0.23%~0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are
available at: this https URL

通过采用自适应梯度剪切、比例重量标准化和专门的瓶颈块等技术，本研究将 BN-Free 训练的框架扩展到二进制神经网络训练，并首次证明可以完全从 BNN 的训练和推断中去除 BN 层，而不会丧失性能。