Vector Quantized Variational AutoEncoder (VQVAE) From Scratch

Code: https://github.com/priyammaz/PyTorch-...

To continue on our AutoEncoder adventure, we move onto the VQVAE! We wont spend too much time here training the models, we will just look at the results, as the training code is basically identical to everything we did earlier in our AutoEncoder Tutorial    • Intro To AutoEncoders: Compression is Ever...   and our VAE tutorial    • Variational AutoEncoders (VAE) Implementation  .

Quantization is a powerful tool, especially leveraged in Neural Speech processing and Generative Models, so I wanted to give an introduction here! Its all about keeping backprop alive!

Timestamps:
00:00:00 Introduction
00:01:06 KMeans
00:05:10 Review AutoEncoders
00:07:00 What is VQVAE?
00:16:00 Visualize Broken Backprop
00:22:40 Straight Through Gradients Estimator
00:28:00 Visualize Straight Through Estimator
00:31:19 Wheres the VAE? Derive ELBO for VQVAE
00:38:25 Codebook + Commitment Loss
00:43:00 Implement the Vector Quantizer
01:08:00 Implement the LinearVQVAE
01:19:00 Plotting the Embeddings
01:22:40 Implement a ConvVQVAE
01:35:40 Recap

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