  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 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
state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.
