From Autoencoders to Variational Autoencoders: Improving the Encoder

Autoencoders have a number of limitations for generative tasks. That’s why they need a power-up to become Variational Autoencoders. In this video, I explain the first step to transform a vanilla autoencoder into a VAE. Specifically, I discuss how VAEs use multivariate normal distributions to encode input data into a latent space and why this is awesome for generative tasks. Don’t worry - I also explain what multivariate normal distributions are! =============================== Slide deck: https://github.com/musikalkemist/gene... Join The Sound Of AI Slack community: https://valeriovelardo.com/the-sound-... =============================== Interested in hiring me as a consultant/freelancer? https://valeriovelardo.com/ Follow Valerio on Facebook:   / thesoundofai   Connect with Valerio on Linkedin:   / valeriovelardo   Follow Valerio on Twitter:   / musikalkemist   =============================== Content 0:00 Intro 0:32 Issues with vanilla AEs 1:03 From AEs to VAEs 1:46 Encoder mapping: AEs vs VAEs 2:40 Univariate normal distribution 11:37 Multivariate normal distrivution 16:13 Usage of multivariate normal distribution in VAEs 22:06 How multivariate normal distribution solves discontinuities in VAEs