title: Adversarially Learned Inference
abstract: We introduce the adversarially learned inference (ALI) model, which jointly
  learns a generation network and an inference network using an adversarial
  process. 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 that is trained to distinguish between
  joint latent/data-space samples from the generative network and joint samples
  from the inference network.  We illustrate the ability of the model to learn
  mutually coherent inference and generation networks through the inspections of
  model samples and reconstructions and confirm the usefulness of the learned
  representations by obtaining a performance competitive with other recent
  approaches on the semi-supervised SVHN task.
authors: Vincent Dumoulin Ishmael Belghazi Ben Poole Alex Lamb Martin Arjovsky Olivier Mastropietro Aaron Courville
hIndex: Aaron Courville 79