Adversarially Learned Inference
This work addresses the challenge of learning coherent latent representations for generative models, which is incremental in combining adversarial training with inference.
The paper tackles the problem of jointly learning generation and inference networks by introducing an adversarial process, achieving competitive performance on semi-supervised SVHN and CIFAR10 tasks.
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.