  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.
