  This paper introduces SelfMatch, a semi-supervised learning method that
combines the power of contrastive self-supervised learning and consistency
regularization. SelfMatch consists of two stages: (1) self-supervised
pre-training based on contrastive learning and (2) semi-supervised fine-tuning
based on augmentation consistency regularization. We empirically demonstrate
that SelfMatch achieves the state-of-the-art results on standard benchmark
datasets such as CIFAR-10 and SVHN. For example, for CIFAR-10 with 40 labeled
examples, SelfMatch achieves 93.19% accuracy that outperforms the strong
previous methods such as MixMatch (52.46%), UDA (70.95%), ReMixMatch (80.9%),
and FixMatch (86.19%). We note that SelfMatch can close the gap between
supervised learning (95.87%) and semi-supervised learning (93.19%) by using
only a few labels for each class.
