Learning Nearby Outliers with Variational Autoencoders

This work aims to facilitate visualization of nearby outliers by training a network to reconstruct outliers that are simulated via feature-level dropout. By introducing outliers into the latent space of variational autoencoders with a more lenient penalty, interpolation can be used to intensify or correct deviations from normal.