The jupyter notebook included in this repository covers an implementation of Importance Weighted Autoencoders (IWAE) as detailed in https://arxiv.org/abs/1509.00519.
The implementation is done in Tensorflow 2.1.0 and tested on the binarized MNIST dataset.
The following theoretical and experimental explorations are covered in this implementation:
- Theory behind IWAE
- Choice of initialization strategy
- Representation learning capabilities of IWAE
- Effect of tighter IWAE bounds
- Large-scale IWAE bound performance