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.