  Autoencoders are widely used for unsupervised learning and as a
regularization scheme in semi-supervised learning. However, theoretical
understanding of their generalization properties and of the manner in which
they can assist supervised learning has been lacking. We utilize recent
advances in the theory of deep learning generalization, together with a novel
reconstruction loss, to provide generalization bounds for autoencoders. To the
best of our knowledge, this is the first such bound. We further show that,
under appropriate assumptions, an autoencoder with good generalization
properties can improve any semi-supervised learning scheme. We support our
theoretical results with empirical demonstrations.
