Importance weighted autoencoder (IWAE)

A minimum implementation of importance weighted autoencoder from Burda et al. (2016). https://arxiv.org/abs/1509.00519

In Pytorch.

Results

Negative log-likelihood of test data using k importance samples after 50 epochs.

k	Negative Log-Likelihood
1	86.886
5	81.439
50	79.008
1000	77.317

Notes:

1. I used the original MNIST dataset. You may want to download the binarized version of MNIST referenced in the paper.
2. At this time torchvision cannot automatically download MNIST dataset. Apparently this is a server side issue. You can however download it manually and then set the root arg of torchvision.datasets.MNIST function with the proper local dataset directory. Keep the download=True flag.
3. I did not use the schedule for the Beta coefficient of ADAM optimizer used in the paper.