SYSTEMS AND METHODS FOR TRAINING GENERATIVE MACHINE LEARNING MODELS WITH SPARSE LATENT SPACES

摘要

Generative machine learning models, such as variational autoencoders, with comparatively sparse latent spaces are provided. Continuous latent variables are activated and/or inactivated based on a state of the latent space. Activation may be controlled by corresponding binary latent variables and/or by rectification of probability distributions defined over the latent space. Sparsification may be supported by normalization of terms, such as providing an L1 or L2 prior.