  In this work, we propose a simple yet effective meta-learning algorithm in
semi-supervised learning. We notice that most existing consistency-based
approaches suffer from overfitting and limited model generalization ability,
especially when training with only a small number of labeled data. To alleviate
this issue, we propose a learn-to-generalize regularization term by utilizing
the label information and optimize the problem in a meta-learning fashion.
Specifically, we seek the pseudo labels of the unlabeled data so that the model
can generalize well on the labeled data, which is formulated as a nested
optimization problem. We address this problem using the meta-gradient that
bridges between the pseudo label and the regularization term. In addition, we
introduce a simple first-order approximation to avoid computing higher-order
derivatives and provide theoretic convergence analysis. Extensive evaluations
on the SVHN, CIFAR, and ImageNet datasets demonstrate that the proposed
algorithm performs favorably against state-of-the-art methods.
