Adversarially Learned Inference

Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville

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.

TL;DR: We present and adverserially trained generative model with an inference network. Samples quality is high. Competitive semi-supervised results are achieved.

Conflicts: umontreal.ca

Keywords: Computer vision, Deep learning, Unsupervised Learning, Semi-Supervised Learning

Community Implementations: [ 10 code implementations](https://www.catalyzex.com/paper/arxiv:1606.00704/code)