The Great Autoencoder Bake Off
“Another article comparing types of autoencoders?”, you may think. “There are already so many of them!”, you may think. “How does he know what I am thinking?!”, you may think. While the first two statements are certainly appropriate reactions - and the third a bit paranoid - let me explain my reasons for this article. There are indeed articles comparing some autoencoders to each other (e.g. [1], [2], [3]), I found them lacking something. Most only compare a hand full of types and/or only scratch the surface of what autoencoders can do. Often you see only reconstructed samples, generated samples, or latent space visualization but nothing about downstream tasks. I wanted to know if a stacked autoencoder is better than a sparse one for anomaly detection or if a variational autoencoder learns better features for classification than a vector-quantized one. Inspired by this repository I found, I took it into my own hands, and thus this blog post came into existence. ...