Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials | npj Computational Materials
convolutional neural networks - What makes GAN or VAE better at image generation than NN that directly maps inputs to images - Artificial Intelligence Stack Exchange
Selection of GAN vs Adversarial Autoencoder models - GeeksforGeeks
Variational Autoencoder with Pytorch | by Eugenia Anello | DataSeries | Medium
Comparison of adversarial and variational autoencoder on MNIST. The... | Download Scientific Diagram
GANs vs. Autoencoders: Comparison of Deep Generative Models | by Matthew Stewart | Towards Data Science
Deep Generative Models: Practical Comparison Between Variational Autoencoders and Generative Adversarial Networks