Understanding Variational Autoencoder | VAE Explained

In this video I deep dive into Variational Autoencoder (VAE) . If you're interested in understanding the inner workings of Variational Autoencoders, and how it differs from traditional autoencoder, you're in the right place.

🔍 In this video, we'll cover the following key points:
What is a Variational Autoencoder (VAE) and how does it work?
Difference between Autoencoder and Variational Autoencoder.
The loss function used in Variational Autoencoder to optimize their training.
Building your very own Variational Autoencoder
Conditional VAE (Conditional Variational Autoencoder)


⏱️ Timestamps
00:15 Video Highlights
00:32 Autoencoders
01:49 Need for Variational Auto Encoder
02:49 Transitioning to VAE from AutoEncoder
05:21 Modelling Data Generation in Variational AutoEncoder
06:18 Deriving Objective and Loss of VAE
08:24 Summary of Variational AutoEncoder Architecture
08:58 Conditional VAE

Resources Used in Making Video

Helpful Links
KL Divergence

Computing P(x)

Background Track - Fruits of Life by Jimena Contreras

[bhanutejanellore]

This is the best content on VAE i saw in YouTube. You must do more videos!

[douwedb]

Really nice video, thanks a lot for this great explanation!

[ronnieleon]

From the video it is evident that using a variational autoencoder for image de-noising works better compared to just using an autoencoder as the latent representation of the images generated by the encoder in an autoencoder is mapped to a point instead of a latent space with a distribution of the latent masks generated by the encoder.

[nitishgupta]

I need a super like button for this explanation. Just one suggestion, you should consider using a more unique name for the channel. It's tough finding it by a simple search.

[random_op]

Great video sir
Sir, can you make a video explaining all probability and statistics required for understanding generative models thoroughly. Or you can suggest some beginner friendly resources to master those?
Lacking those, I am unable to grasp the whole concept of VAE, GAN, Diffusion models ...

[inkxmpetentertyp]

Can you implement this Model also for music generation? I assume you can but since you talk only about image - generating I thought I better ask myself.

[hientq]

7:49 I'm not really understand why the underline part is constant, can u explain a bit?

[alexijohansen]

Thank you, but assumes a lot of math knowledge.