  The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializing the weights of the network and after these weights are
refined with the use of supervised data. On the other hand adversarial noise
has improved the results of clas- sical supervised learning. Recently, a new
neural network topology called Ladder Network, where the key idea is based in
some properties of hierar- chichal latent variable models, has been proposed as
a technique to train a neural network using supervised and unsupervised data at
the same time with what is called semi-supervised learning. This technique has
reached state of the art classification. In this work we add adversarial noise
to the ladder network and get state of the art classification, with several
important conclusions on how adversarial noise can help in addition with new
possible lines of investi- gation. We also propose an alternative to add
adversarial noise to unsu- pervised data.
