Vae-Pytorch

This repository has some of my works on VAEs in Pytorch. At the moment I am doing experiments on usual non-hierarchical VAEs. ConvVAE architecture is based on this repo, and MLPVAE on this.

Currently implemented VAEs:

1. Standard Gaussian based VAE
2. Gamma reparameterized rejection sampling by Naesseth et al.. This implementation is based on the work of Mr. Altosaar.

How to run

Example 1:

$ python3 main.py

Example 2:

$ python3 main --model normal --epochs 5000

Example 3:

$ python3 main --model gamma --b_size 256

Example 4:

$ python3 main --model gamma --dataset mnist --z_dim 5

Usage

usage: main.py [-h] [--model M] [--epochs N] [--dataset D]
               [--b_size  B] [--z_dim   Z]

optional arguments:
  -h, --help        show this help message and exit
  --model   M       vae model to use: gamma | normal, default is normal
  --epochs N        number of total epochs to run, default is 10000
  --dataset D       dataset to run experiments (cifar-10 or mnist)
  --b_size  B       batch size
  --z_dim   Z       size of the latent space

Experiments

Qualitative results

• z_dim = 4
• b_size = 128

Cifar-10 validation samples (Gaussian VAE)

Cifar-10 validation samples (Gamma VAE)

Mnist validation samples (Gaussian VAE)

Mnist validation samples (Gamma VAE)

Binary Mnist validation samples (Gaussian VAE)

Binary Mnist validation samples (Gamma VAE)

Quantitative results

Train

	CIFAR-10	MNIST	Binary MNIST
KL
Recons

Validation

	CIFAR-10	MNIST	Binary MNIST
KL
Recons
M Likelihood

Acknowledgments

• To Mr. Altosaar (@altosaar) for helping me on some questions I had in his implementation and several other questions in the subject of VAEs.