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:
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Funny explanation of KL-Divergence:
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#tensorflow2 #vae
