Variational Autoencoders (VAEs)

Question : Describe the encoder and decoder networks in a VAE. What are their roles, and what do they output? What is the reparameterization trick, and why is it essential for training VAEs with gradient-based optimization? Illustrate with equations. Compare the training objective of VAEs with GANs. What does each optimize, and what are the implications for sample quality?

Video - Introduction to VAE - Martin Keen IBM

Martin Keen LinkedIn

Video - Stanford CS229: Variational Autoencoders (VAEs) Anand Avati

Introduction to Auto Encoders

  1. Autoencoders
  2. Expectation Maximization (EM)
  3. MCMC (Markov Chain Monte Carlo) Expectation Maximization
  4. Variational Inference
  5. Variatoinal Expectation Maximization (VEM)
  6. Variational Auto Encoders (VAE)

Video - CMU CS 15-418/618: Variational Autoencoders (VAEs) 1

Video - CMU CS 15-418/618: Variational Autoencoders (VAEs) 2

Introduction :

Autoencorders are unsupervised neural networks that learn to compress data into a lower-dimensional representation and then reconstruct the original data from this representation. They consist of two main components: an encoder and a decoder.

graph LR
    A[Input Data] --> B[Encoder]
    B --> C[Latent Space Representation]
    C --> D[Decoder]
    D --> E[Reconstructed Data]
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#bfb,stroke:#333,stroke-width:2px
    style D fill:#ffb,stroke:#333,stroke-width:2px
    style E fill:#fbb,stroke:#333,stroke-width:2px

Tushar Kumar’s Explanation of VAE

https://www.linkedin.com/in/tushar-kumar-40299b19/