Abstract: A method and system for training a model for image generation. The model includes a hybrid variational auto-encoder (VAE)—generative adversarial network (GAN) framework. The method includes the steps of: multiple input of an input image into the VAE which outputs in response multiple distinct output image samples, determining the best of the multiple output image samples as a best-of-many sample, the best-of-many sample having the minimum reconstruction cost, and training the model based on a predefined training objective, the predefined training objective integrating the best-of-many sample reconstruction cost and a GAN-based synthetic likelihood term.

Abstract: The learning of probability distributions of data enables various applications, including but not limited to data synthesis and probability inference. A conditional non-linear normalizing flow model, and a system and method for training said model, are provided. The normalizing flow model may be trained to model unknown and complex conditional probability distributions which are at the heart of many real-life applications.

Abstract: A machine learnable system is described. A conditional normalizing flow function maps a latent representation to a base point in a base space conditional on conditioning data. The conditional normalizing flow function is a machine learnable function and trained on a set of training pairs.