Lateral Connections in Denoising Autoencoders Support Supervised Learning

Tapani Raiko Aalto University, Finland We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layerwise pretraining. It improves the state of the art significantly in the permutationinvariant MNIST classification task.