A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures that are otherwise intractable, it has been challenging both to generically improve upon Batch Normalization and to understand specific circumstances that lend themselves to other enhancements. In this paper, we identify four improvements to the generic form of Batch Normalization and the circumstances under which they work, yielding performance gains across all batch sizes while requiring no additional computation during training. These contributions include proposing a method for reasoning about the current example in inference normalization statistics which fixes a training vs. inference discrepancy; recognizing and validating the powerful regularization effect of Ghost Batch Normalization for small and medium batch sizes; examining the effect of weight decay regularization on the scaling and shifting parameters; and identifying a new normalization algorithm for very small batch sizes by combining the strengths of Batch and Group Normalization. We validate our results empirically on four datasets: CIFAR-100, SVHN, Caltech-256, and ImageNet.

本文探讨了神经网络优化中常见的批量标准化方法，并提出了四种改善方法，包括基于推理标准化统计的推理现有实例的方法、小批量大小下有效的 Ghost Batch Normalization 正则化方法、权重衰减正则化对 scaling 和 shifting 参数 gamma 和 beta 的影响、并结合批量标准化和分组标准化的方法解决小批量规模的问题。这些方法可以提高神经网络在六个数据集上的性能表现。

提高批标准化的四个必知技巧