Recent work has shown that using unlabeled data in semi-supervised learning is not always beneficial and can even hurt generalization, especially when there is a class mismatch between the unlabeled and labeled examples. We investigate this phenomenon for image classification on the CIFAR-10 and the ImageNet datasets, and with many other forms of domain shifts applied (e.g. salt-and-pepper noise). Our main contribution is Split Batch Normalization (Split-BN), a technique to improve SSL when the additional unlabeled data comes from a shifted distribution. We achieve it by using separate batch normalization statistics for unlabeled examples. Due to its simplicity, we recommend it as a standard practice. Finally, we analyse how domain shift affects the SSL training process. In particular, we find that during training the statistics of hidden activations in late layers become markedly different between the unlabeled and the labeled examples.

本文介绍了Split Batch Normalization技术用于提高半监督学习的效果，并在CIFAR-10和ImageNet数据集上进行实证分析，发现当训练数据包含来自不同分布的未标注数据时，使用该技术可以改善模型的表现。同时，还分析了域漂移对SSL训练过程的影响。

Split Batch Normalization: 改善领域偏移下的半监督学习