Adversarial feature distribution alignment for semi-supervised learning

Abstract

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 100x less unlabeled samples (500 labeled samples). Finally, we provide a theoretical insight why feature distribution misalignment occurs and show that our method reduces it. © 2020 Elsevier