Augmentation-Autoencoders

Implementation of Simple Autoencoders for augmentation. Variational Deep Learning is a method of deep learning where we use Neural Networks to generate data, instead of drawing conclusions from it.

We have currrently implemented four Autoencoders:

• Vanilla Autoencoder on MNIST dataset.
• Denoising Autoencoder on MNIST dataset.
• Sparse Autoencoder on MNIST dataset.
• Contractive Autoencoder on MNIST dataset.