Variational Autoencoders

Simple autoencoders can be considered as a data structures that memorize an image and regenerate it. But we want to generate new images not a data structure to memorize the image. To do it we add stochastic nature to our model by introducing a latent vector that is generated using the outputs of the encoder network. Where outputs of the encoder network represent the mean and standard deviation of the latent gaussian vector

Variational Autoencoder
Source: Variational Autoencoders Explained

Dataset

The VAE is trained on Cifar-10 dataset for about 30 epochs.

CIFAR-10 Images

Output

The VAE generated vague images on start but got better with time. To see the results of your training from the VAE-demo notebook - Start a new terminal and execute command:

tensorboard --logdir=./vae_400

and then go to <your-public-ip-adress>:6000 (if training on an instance) or to <localhost>:6000 (if training on local system).

Final Output

VAE outputs