Task: Explore the use of autoencoders for
- Data compression
- Data generation
- Data interpolation
Dataset:
MNIST
Libraries: PyTorch, torchvision
Learning objectives:
- Build and train the following types of autoencoders
- Sparse autoencoder with L1 penalty
- Sparse autoencoder with KL penalty
- Contractive Autoenoder
- Variational Autoenoder
- Explore the quality of their learned latent space