  Deep learning based semi-supervised learning (SSL) algorithms have led to
promising results in recent years. However, they tend to introduce multiple
tunable hyper-parameters, making them less practical in real SSL scenarios
where the labeled data is scarce for extensive hyper-parameter search. In this
paper, we propose a novel meta-learning based SSL algorithm (Meta-Semi) that
requires tuning only one additional hyper-parameter, compared with a standard
supervised deep learning algorithm, to achieve competitive performance under
various conditions of SSL. We start by defining a meta optimization problem
that minimizes the loss on labeled data through dynamically reweighting the
loss on unlabeled samples, which are associated with soft pseudo labels during
training. As the meta problem is computationally intensive to solve directly,
we propose an efficient algorithm to dynamically obtain the approximate
solutions. We show theoretically that Meta-Semi converges to the stationary
point of the loss function on labeled data under mild conditions. Empirically,
Meta-Semi outperforms state-of-the-art SSL algorithms significantly on the
challenging semi-supervised CIFAR-100 and STL-10 tasks, and achieves
competitive performance on CIFAR-10 and SVHN.
