Variational Autoencoder | Introduction and Workshop
Variational Autoencoder is a more advance version of autoencoder. Instead of storing the latent vector directly in the neural network, it added another layer of gaussian function to allow for a more general representation of those latent vector. Typically, it allows for a better generation of data than GAN in certain situation. In this video I tried to walkthrough some basic introduction of VAE, how to make them in R, and how they were used in research.
References
https://towardsdatascience.com/unders...
https://www.tensorflow.org/tutorials/...
https://keras.io/examples/generative/...
Slides
https://docs.google.com/presentation/...
Script
https://github.com/brandonyph/LiquidB...
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