  In this work, we propose a simple yet effective semi-supervised learning
approach called Augmented Distribution Alignment. We reveal that an essential
sampling bias exists in semi-supervised learning due to the limited number of
labeled samples, which often leads to a considerable empirical distribution
mismatch between labeled data and unlabeled data. To this end, we propose to
align the empirical distributions of labeled and unlabeled data to alleviate
the bias. On one hand, we adopt an adversarial training strategy to minimize
the distribution distance between labeled and unlabeled data as inspired by
domain adaptation works. On the other hand, to deal with the small sample size
issue of labeled data, we also propose a simple interpolation strategy to
generate pseudo training samples. Those two strategies can be easily
implemented into existing deep neural networks. We demonstrate the
effectiveness of our proposed approach on the benchmark SVHN and CIFAR10
datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.
