Variational AutoEncoders (VAE) Implementation

Code: https://github.com/priyammaz/PyTorch-Adventures/blob/main/PyTorch%20for%20Generation/AutoEncoders/Intro%20to%20AutoEncoders/Variational_AutoEncoders.ipynb Today we will implement the Variational AutoEncoder! Previously we derived the loss function for the VAE here https://www.youtube.com/watch?v=jJZadDULoH4 This is pretty similar to the basic AutoEncoder, just some new tricks to get it all to work! I think VAEs are some of the coolest things about Neural Networks, but they have some challenges and limitations that I want to explore today! Timestamps: 00:00:00 Introduction 00:01:00 AutoEncoders vs Variational AutoEncoders 00:02:45 How do VAEs map to Gaussian? 00:09:15 Reparamaterization Trick 00:29:30 LogVariance 00:31:16 Linear VAE 00:45:20 Writing the VAE Loss Function 00:58:20 Training a VAE 01:00:40 Comparing KL Weights 01:02:15 Generating New Samples 01:10:54 Convolutional VAE 01:26:44 ConvVAE Results 01:31:45 Perceptual Loss Functions 01:33:33 Recap Socials! X https://twitter.com/data_adventurer Instagram https://www.instagram.com/nixielights/ Linkedin https://www.linkedin.com/in/priyammaz/ 🚀 Github: https://github.com/priyammaz 🌐 Website: https://www.priyammazumdar.com/