Uploaded January 2021 | Updated September 2026, 2 weeks ago

---

Autoencoders have a number of limitations for generative tasks. That’s why they need a power-up to convert them into Variational Autoencoders. In this video, I explain the second step to transform a vanilla autoencoder into a VAE. Specifically, I discuss how VAEs add a regularization term to their loss function, implemented through the Kullback-Leibler Divergence.

===============================

Slide deck:
github.com/musikalkemist/generating-sound-with-neural-networks/blob/main/10%20From%20AEs%20to%20VAEs%20Part%202/From%20Autoencoders%20to%20Variational%20Autoencoders%20The%20Loss%20Function.pdf

Join The Sound Of AI Slack community:
valeriovelardo.com/the-sound-of-ai-community

===============================

Interested in hiring me as a consultant/freelancer?
valeriovelardo.com

Follow Valerio on Facebook:
facebook.com/TheSoundOfAI

Connect with Valerio on Linkedin:
linkedin.com/in/valeriovelardo

Follow Valerio on Twitter:
twitter.com/musikalkemist

===============================

Content
0:00 Intro
0:44 Autoencoder loss
1:42 VAE loss
3:08 Kullback-Leibler Divergence
8:02 Weighting the loss function
9:45 What's next?