Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey

Abstract

This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial autoencoders, and their variants. We start with explaining adversarial learning and the vanilla GAN. Then, we explain the conditional GAN and DCGAN. The mode collapse problem is introduced and various methods, including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and Wasserstein GAN, are introduced for resolving this problem. Then, maximum likelihood estimation in GAN are explained along with f-GAN, adversarial variational Bayes, and Bayesian GAN. Then, we cover feature matching in GAN, InfoGAN, GRAN, LSGAN, energy-based GAN, CatGAN, MMD GAN, LapGAN, progressive GAN, triple GAN, LAG, GMAN, AdaGAN, CoGAN, inverse GAN, BiGAN, ALI, SAGAN, Few-shot GAN, SinGAN, and interpolation and evaluation of GAN. Then, we introduce some applications of GAN such as image-to-image translation (including PatchGAN, CycleGAN, DeepFaceDrawing, simulated GAN, interactive GAN), text-to-image translation (including StackGAN), and mixing image characteristics (including FineGAN and MixNMatch). Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.

arXiv commentTo appear as a part of an upcoming textbook on dimensionality reduction and manifold learning

References in corpus (39)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Distributed Representations of Words and Phrases and their Compositionality
• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Generative Adversarial Networks: An Overview
• Self-Attention Generative Adversarial Networks
• Generative Adversarial Text to Image Synthesis
• Conditional Image Generation with PixelCNN Decoders
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Which Training Methods for GANs do actually Converge?
• Estimating divergence functionals and the likelihood ratio by convex risk minimization
• Adversarially Learned Inference
• Semi-Supervised Learning with Generative Adversarial Networks
• Unrolled Generative Adversarial Networks
• How Generative Adversarial Networks and Their Variants Work: An Overview
• Generative Moment Matching Networks
• Learning What and Where to Draw
• How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
• The Numerics of GANs
• Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
• The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
• A family of statistical symmetric divergences based on Jensen's inequality
• Generative Multi-Adversarial Networks
• Bayesian GAN
• A Convex Duality Framework for GANs
• PixelGAN Autoencoders
• KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
• GANGs: Generative Adversarial Network Games
• Few-shot Image Generation via Cross-domain Correspondence
• Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
• Training Triplet Networks with GAN
• Boosted Generative Models
• Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
• Creating High Resolution Images with a Latent Adversarial Generator
• Fitting A Mixture Distribution to Data: Tutorial
• Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey
• xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems
• Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey