What The Heck Are VAE-GANs?

Yep, you read the title correctly. While a few friends of mine are vegans, none of them knew anything about VAE-GANs. VAE-GAN stands for Variational Autoencoder- Generative Adversarial Network (that is one heck of a name.) Before we get started, I must confess that I am no expert in this subject matter (I don’t have PhD in electrical engineering, just sayin’). But after reading several research papers and watching Ian Goodfellow’s 30-minute long intro to GANs, here is a short (yet concise) summary of my major takeaways:

Image reconstructed by VAE and VAE-GAN compared to their original input images

Variational Autoencoders (VAEs)

The simplest way of explaining variational autoencoders is through a diagram. Alternatively, you can read Irhum Shafkat’s excellent article on Intuitively Understanding Variational Autoencoders. At this point I assume you have a general idea of what unsupervised learning and generative models are. The textbook definition of a VAE is that it “provides probabilistic descriptions of observations in latent spaces.” In plain English, this means VAEs store latent attributes as probability distributions.

“Variational autoencoders”- Jeremy Jordan

Each input image has features that can normally be described as single, discrete values. Variational autoencoders describe these values…