2019/02/04 by Baruch Epstein, Epstein, Baruch, Ron Meir +1 · 2 citations
Computer Science · #Domain Adaptation and Few-Shot Learning #Anomaly Detection Techniques and Applications #AI in cancer detection
paper · pdf · doi:10.48550/arxiv.1902.01449
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.