Скачать или смотреть CODE Variational Autoencoder (VAE) w/ KL-Divergence

A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation space.

Unlike a traditional autoencoder, which maps the input onto a latent vector, a VAE maps the input data into the parameters of a probability distribution, such as the mean and variance of a Gaussian. This approach produces a continuous, structured latent space, which is useful for image generation.

official link and COLAB NB:
https://www.tensorflow.org/tutorials/...

https://colab.research.google.com/git...

Funny explanation of KL-Divergence:
https://towardsdatascience.com/light-...

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#deeplearning
#tensorflow2 #vae