Adversarial learning for generative models and inference

Generative Adversarial Networks (GANs) pose the learning of a generative model as an adversarial game between a discriminator, trained to distinguish true and generated samples, and a generator, trained to try to fool the discriminator. Since their introduction in 2014, GANs have been the subject of a surge of research activity, due to their ability to produce realistic samples of highly structured data such as natural images.

In this talk I will present a brief introduction to Generative Adversarial Networks (GANs), and discuss some of our recent work in improving the stability of training of GAN models. I will also describe our recent work on adversarially learned inference (ALI), which jointly learns a generation network and an inference network using a GAN-like adversarial process. In ALI, the generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network.