Vector-Quantized VAEs were proposed in 2017. Since its inception, it has pushed the field of high-quality image generation to a great extent. Its recipes like discrete latent space optimization, codebook sampling, etc. have gone to later become essential blocks for modern models like VQ-GAN, DALL-E, etc.
In my latest Keras example, I present an implementation VQ-VAEs including the subsequent PixelCNN part for image reconstruction and generation. I’ve included crucial pieces of visualizations as well to make it fun and interesting.

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Here is the link to my example:

  
      

      keras.io
  

  
    

Keras documentation: Vector-Quantized Variational Autoencoders 12