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.

翻译：在这项工作中,我们提议在半监督的学习中采用简单而有效的元学习算法。我们注意到,大多数现有的基于一致性的方法都因过于适应和有限的典型概括化能力而受到影响,特别是在仅用少量标签数据进行培训时。为了缓解这一问题,我们提议通过使用标签信息来学习普及正规化术语,并以元学习方式优化问题。具体地说,我们寻找未贴标签数据的假标签,以便模型能够将标签数据广泛归纳在作为嵌套式优化问题的标签数据上。我们利用假标签与正规化术语之间的连接的元等级来解决这个问题。此外,我们引入了简单的第一阶近似法,以避免计算更高顺序的衍生物并提供理论趋同分析。对SVHN、CIFAR和图像网络数据集的广泛评价表明,拟议的算法对州-艺术方法有利。