Adversarial Ladder Networks

The use of unsupervised data in addition to supervised data has lead to a significant improvement when training discriminative neural networks. 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. Recently, a new neural network topology called Ladder Network, where the key idea is based in some properties of hierarchichal 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. On the other hand adversarial noise has improved the results of classical supervised learning. 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 investigation. We also propose an alternative to add adversarial noise to unsupervised data.