Virtual Adversarial Training (VAT) has shown impressive results among
recently developed regularization methods called consistency regularization.
VAT utilizes adversarial samples, generated by injecting perturbation in the
input space, for training and thereby enhances the generalization ability of a
classifier. However, such adversarial samples can be generated only within a
very small area around the input data point, which limits the adversarial
effectiveness of such samples. To address this problem we propose LVAT (Latent
space VAT), which injects perturbation in the latent space instead of the input
space. LVAT can generate adversarial samples flexibly, resulting in more
adverse effects and thus more effective regularization. The latent space is
built by a generative model, and in this paper, we examine two different type
of models: variational auto-encoder and normalizing flow, specifically Glow. We
evaluated the performance of our method in both supervised and semi-supervised
learning scenarios for an image classification task using SVHN and CIFAR-10
datasets. In our evaluation, we found that our method outperforms VAT and other
state-of-the-art methods.

本文提出了一种名为 LVAT 的新的规则化方法，将扰动注入潜在空间以生成理解性更强的对抗样本，并在图像分类任务的超视觉和半监督学习情景中使用 SVHN 和 CIFAR-10 数据集验证其性能，证明其优于其他现有方法及 VAT。