# It Takes (Only) Two: Adversarial Generator-Encoder Networks

## TL;DR

This paper introduces a novel autoencoder architecture trained via adversarial learning directly between the encoder and generator, improving sample quality without external mappings, and enabling unsupervised training for high-quality generation and inference.

## Contribution

The paper proposes a new adversarial generator-encoder network with a direct encoder-vs-generator game, simplifying training and enhancing sample and reconstruction quality.

## Key findings

- Achieves high-quality samples comparable to complex architectures
- Enables unsupervised training for generation and inference
- Simplifies adversarial training by removing external mappings

## Abstract

We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures.