Semi-supervised learning (SSL) partially circumvents the high cost of
labeling data by augmenting a small labeled dataset with a large and relatively
cheap unlabeled dataset drawn from the same distribution. This paper offers a
novel interpretation of two deep learning-based SSL approaches, ladder networks
and virtual adversarial training (VAT), as applying distributional smoothing to
their respective latent spaces. We propose a class of models that fuse these
approaches. We achieve near-supervised accuracy with high consistency on the
MNIST dataset using just 5 labels per class: our best model, ladder with
layer-wise virtual adversarial noise (LVAN-LW), achieves 1.42% +/- 0.12 average
error rate on the MNIST test set, in comparison with 1.62% +/- 0.65 reported
for the ladder network. On adversarial examples generated with L2-normalized
fast gradient method, LVAN-LW trained with 5 examples per class achieves
average error rate 2.4% +/- 0.3 compared to 68.6% +/- 6.5 for the ladder
network and 9.9% +/- 7.5 for VAT.

基于分布平滑的半监督深度学习方法和梯度升级生成虚拟对抗训练方法被提出用于降低数据标签的高成本，两种方法被成功整合实现了接近监督准确度。