Deep diffusion autoencoders

International Joint Conference on Neural Networks, celebrada en 2019 en Budapest© 2019 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Extending work by Mishne et al., we propose Deep Diffusion Autoencoders (DDA) that learn an encoder-decoder map using a composite loss function that simultaneously minimizes the reconstruction error at the output layer and the distance to a Diffusion Map embedding in the bottleneck layer. These DDA are thus able to reconstruct new patterns from points in the embedding space in a way that preserves the geometry of the sample and, as a consequence, our experiments show that they may provide a powerful tool for data augmentation.With partial support from Spain’s grants TIN2016-76406-P and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL-Ayudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAM–ADIC Chair for Data Science and Machine Learning. We also gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM