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A minimal pytorch implementation of VAE, IWAE, MIWAE

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pytorch-vae

A minimal pytorch implementation of VAE, IWAE, and MIWAE. We followed the experimental details of the IWAE paper.

Usage

You should be able to run experiments right away. First create a virtual environment using pipenv:

pipenv install

To run experiments, you simply have to use:

pipenv run python main.py <options>

Example commands

For original VAE:

pipenv run python main.py

To also make figures (reconstruction, samples):

pipenv run python main.py --figs

For IWAE with 5 importance samples:

pipenv run python main.py --importance_num=5

For MIWAE(16, 4):

pipenv run python main.py --mean_num=16 --importance_num=4

See the config file for more options.

Results

Quantitative results on dynamically binarized MNIST

Method NLL (this repo) NLL (IWAE paper) NLL (MIWAE paper) comments
VAE 87.01 86.76 -
MIWAE(5, 1) 86.45 86.47 - listed as VAE with k=5
MIWAE(1, 5) 85.18 85.54 - listed as IWAE with k=5
MIWAE(64, 1) 86.07 - 86.21 listed as VAE
MIWAE(16, 4) 84.99 - -
MIWAE(8, 8) 84.69 - 84.97
MIWAE(4, 16) 84.52 - 84.56
MIWAE(1, 64) 84.37 - 84.52 listed as IWAE

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