Discriminative Regularized Deep Generative Models for SemiSupervised Learning Deep generative models (DGMs) have shown strong performance in semisupervised learning (SSL), which incorporate discrete class information into the learning process. Yet existing methods generally overfit to the given labeled data, for only considering the conditional probability of labels. In this paper, we propose a novel discriminative regularized deep generative method for SSL, which fully exploits the discriminative and geometric information of data to address the aforementioned issue. Our method introduces the cluster and manifold assumption that maximizes the classification margin between clusters and simultaneously smooths the predictions of the data which is close in the submanifold of each cluster, to regularize the learning of the classifier in DGMs. To derive the regularization based on introduced assumptions, we adopt the generated data of DGMs along with labelled and unlabelled data, to model the data manifold and yield clusters based on the Gumbelsoftmax distribution. Experimental results on both text and image datasets demonstrate the effectiveness and flexibility of our method, and prove that two introduced assumptions are complementary in guiding the classification boundary, thus improving the discriminative ability of the classifier.

Fast Distributional Smoothing for Regularization in CTC Applied to Text Recognition Many recent text recognition studies achieved successful performance by applying a sequentiallabel prediction framework such as connectionist temporal classification. Meanwhile, regularization is known to be essential to avoid overfitting when training deep neural networks. Regularization techniques that allow for semisupervised learning have a greater impact than those that do not. Among widely researched singlelabel regularization techniques, virtual adversarial training (VAT) performs successfully by smoothing posterior distributions around training data points. However, VAT is almost solely applied to singlelabel prediction tasks, not to sequentiallabel prediction tasks. This is because the number of possible candidates in the label sequence exponentially increases with the sequence length, making it impractical to calculate posterior distributions and the divergence between them. Investigating this problem, we have found that there is an easily computable upper bound for divergence. Here, we propose fast distributional smoothing (FDS) as a method for drastically reducing computational costs by minimizing this upper bound. FDS allows regularization at practical computational costs in both supervised and semisupervised learning. An experiment under simple settings confirmed that upperbound minimization decreases divergence. Experiments also show that FDS improves scene text recognition performance and enhances stateoftheart regularization performance. Furthermore, experiments show that FDS enables efficient semisupervised learning in sequentiallabel prediction tasks and that it outperforms a conventional semisupervised method.