We propose a new regularization method based on virtual adversarial loss: a
new measure of local smoothness of the conditional label distribution given
input. Virtual adversarial loss is defined as the robustness of the conditional
label distribution around each input data point against local perturbation.
Unlike adversarial training, our method defines the adversarial direction
without label information and is hence applicable to semi-supervised learning.
Because the directions in which we smooth the model are only "virtually"
adversarial, we call our method virtual adversarial training (VAT). The
computational cost of VAT is relatively low. For neural networks, the
approximated gradient of virtual adversarial loss can be computed with no more
than two pairs of forward- and back-propagations. In our experiments, we
applied VAT to supervised and semi-supervised learning tasks on multiple
benchmark datasets. With a simple enhancement of the algorithm based on the
entropy minimization principle, our VAT achieves state-of-the-art performance
for semi-supervised learning tasks on SVHN and CIFAR-10.

提出了一种基于虚拟对抗损失的正则化方法 - 一种关于条件标签分布的局部平滑度的新度量。虚拟对抗损失被定义为每个数据点周围条件标签分布对本地扰动的鲁棒性。我们的方法定义了无需标签信息的对抗方向，因此适用于半监督学习。在多个基准数据集上，通过熵最小化原理实现的 VAT 在半监督学习任务上实现了 SVHN 和 CIFAR-10 的最新性能。