Autoencoders safari

Convolutional Autoencoders implementations using tensorflow and keras and the MNIST dataset.

In this safari you will see three implementations of autoencoders: a convolutional autoenconder, a convolutional variational autoencoder and a convolutional conditional variational autoencoder.

Convolutional Autoencoder (CA)

convolutional_autoencoder.ipynb: implementation of a convolutional autoencoder.

Both the encoder and the decoder have convolutional layers.
You will also find some plots of the latent space.

Convolutional Variational Autoencoder (CVA)

variational_convolutional_autoencoder.ipynb: implementation of a convolutional variational autoencoder.

We make use of the reparametrization trick.
Both the encoder and the decoder have convolutional layers.
You will also find some plots of the latent space.
At the end you will find how to generate samples using the trained encoder.

Convolutional Conditional Variational Autoencoder (CCVA)

conditional_convolutional_variatinoal_autoencoder.ipynb: implementation of a convolutional conditional variational autoencoder.

We make use of the reparametrization trick.
Both the encoder and the decoder have convolutional layers.
At the end you will find how to generate samples using the trained encoder and custom labels to select the category of the samples.