Training deep neural networks with only a few labeled samples can lead to overfitting. This is problematic in semi-supervised learning where only a few labeled samples are available. In this paper, we show that a consequence of overfitting in SSL is feature distribution misalignment between labeled and unlabeled samples. Hence, we propose a new feature distribution alignment method. Our method is particularly effective when using only a small amount of labeled samples. We test our method on CIFAR-10, SVHN and LSUN. On SVHN we achieve a test error of 3.88% (250 labeled samples) and 3.39% (1000 labeled samples), which is close to the fully supervised model 2.89% (73k labeled samples). In comparison, the current SOTA achieves only 4.29% and 3.74%. On LSUN we achieve superior results than a state-of-the- art method even when using $100\times$ less unlabeled samples (500 labeled samples). Finally, we provide a theoretical insight why feature distribution misalignment occurs and show that our method reduces it.

仅用少量标记的样本训练深度神经网络可能会导致过度拟合。这在只有少量标记样本的半监督学习中是有问题的。在本文中，我们证明了SSL过度拟合的结果是标记和未标记样本之间的特征分布未对齐。因此，我们提出了一种新的特征分布对齐方法。仅使用少量标记样品时，我们的方法特别有效。我们在CIFAR-10，SVHN和LSUN上测试了我们的方法。在SVHN上，我们实现了3.88％（250个标记的样本）和3.39％（1000个标记的样本）的测试误差，接近完全监督的模型2.89％（73k标记的样本）。相比之下，当前的SOTA仅达到4.29％和3.74％。在LSUN上，即使使用$100\times$较少的未标记样品（500个标记样品）。最后，我们提供了一个理论上的见解，为什么会发生特征分布不对齐的情况，并表明我们的方法可以减少这种情况。