# Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey

**Authors:** Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley  
**arXiv:** [2111.13282](https://arxiv.org/abs/2111.13282)  
**Date:** 2021-11-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2111.13282v1/

## Key points

- Generative Adversarial Networks (GANs) and Adversarial Autoencoders are key topics in machine learning and artificial intelligence.
- The tutorial and survey paper by Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, and Mark Crowley provides a comprehensive overview of GANs, adversarial autoencoders, and their variants.
- The paper covers the concept of adversarial learning and discusses the Vanilla GAN architecture as well as advanced variations like Conditional GANs (cGAN) and Deep Convolutional GANs (DCGAN).
- Techniques to address mode collapse in GANs are explored, including Minibatch GAN, Unrolled GAN, BourGAN, Mixture GAN, D2GAN, and Wasserstein GAN.
- Maximum Likelihood Estimation in GANs is discussed along with methodologies such as f-GAN, Adversarial Variational Bayes (AVB), and Bayesian GAN.

## Summary

Generative Adversarial Networks (GANs) and Adversarial Autoencoders have gained significant attention in the field of machine learning and artificial intelligence. In this tutorial and survey paper by Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, and Mark Crowley, a comprehensive overview of GANs, adversarial autoencoders, and their variants is provided. The paper begins by elucidating the concept of adversarial learning and delves into the foundational Vanilla GAN architecture. Subsequently, it explores more advanced variations such as Conditional GANs (cGAN) and Deep Convolutional GANs (DCGAN). The notorious mode collapse issue within GANs is addressed, with a discussion on various techniques like Minibatch GAN, Unrolled GAN, BourGAN, Mixture GAN, D2GAN, and Wasserstein GAN that aim to mitigate this challenge. Moving forward, the paper delves into Maximum Likelihood Estimation in GANs alongside methodologies like f-GAN, Adversarial Variational Bayes (AVB), and Bayesian GAN.