  In recent years, great progress has been made to incorporate unlabeled data
to overcome the inefficiently supervised problem via semi-supervised learning
(SSL). Most state-of-the-art models are based on the idea of pursuing
consistent model predictions over unlabeled data toward the input noise, which
is called consistency regularization. Nonetheless, there is a lack of
theoretical insights into the reason behind its success. To bridge the gap
between theoretical and practical results, we propose a worst-case consistency
regularization technique for SSL in this paper. Specifically, we first present
a generalization bound for SSL consisting of the empirical loss terms observed
on labeled and unlabeled training data separately. Motivated by this bound, we
derive an SSL objective that minimizes the largest inconsistency between an
original unlabeled sample and its multiple augmented variants. We then provide
a simple but effective algorithm to solve the proposed minimax problem, and
theoretically prove that it converges to a stationary point. Experiments on
five popular benchmark datasets validate the effectiveness of our proposed
method.
