Variational AutoEncoder Models

A collection of variational autoencoder models, e.g. VAE, CVAE, InfoVAE, MMDVAE in Tensorflow.

How to use?

• Command 1: python train.py vae_name train
• Command 2: python train.py vae_name generate
• Command 3: python train.py vae_name generate path/to/image

Note: Generated samples will be stored in images/{vae_model}/ directory during training.

Variational Autoencoders

The following papers are just examples on how to use the implemented variational autoencoders.
We did not mean to implement what have been described in each paper.

• VAE: Vanilla Variational AutoEncoder
  • Auto-Encoding Variational Bayes, 2014
  
• CVAE: Conditional Variational AutoEncoder
  • Learning Structured Output Representation using Deep Conditional Generative Models, 2016
  • An Uncertain Future: Forecasting from Static Images using Variational Autoencoders, 2016
  
• InfoVAE: Information Variational Autoencoder
  • Information Maximizing Variational Autoencoders, 2017
  
• MMDVAE: Maximum-Mean Discrepancy Variational AutoEncoder
  • Information Maximizing Variational Autoencoders, 2017
  

Model	Loss Function
VAE
CVAE
InfoVAE
MMDVAE

Results for MNIST

The following results can be reproduced with command:

python train.py vae_name train

Note: 1st and 3rd rows represent the ground truth whereas the 2nd and 4th rows are the generated ones.

Name	Epoch 1	Epoch 15	Epoch 30
VAE
CVAE

Name	Epoch 1	Epoch 2	Epoch 3
InfoVAE
MMDVAE

Dependencies

1. Install miniconda https://docs.conda.io/en/latest/miniconda.html
2. Create an environment conda create --name autoencoder
3. Activate the environment source activate autoencoder
4. Install [Tensorflow] conda install -c conda-forge tensorflow
5. Install [Opencv] conda install -c conda-forge opencv
6. Install [sklearn] conda install -c anaconda scikit-learn
7. Install [matplotlib] conda install -c conda-forge matplotlib

Datasets

If you wanna try new dataset, please make sure you make it in the following way:

• Dataset_main_directory If you wanna try new dataset, please make sure you make it in the following way:
• Dataset_main_directory
  • train_data
    • category_1: (image1, image2, ...)
    • category_2: (image1, image2, ...)
    • ...
  • test_data
    • category_1: (image1, image2, ...)
    • category_2: (image1, image2, ...)
    • ...

The loader.py file will automatically upload all images and their labels (category_i folders)

Acknowledgements

This implementation has been based on the work of the great following repositories:

• https://github.com/hwalsuklee/tensorflow-generative-model-collections
• https://github.com/wiseodd/generative-models
• https://github.com/eriklindernoren/Keras-GAN
• https://github.com/ShengjiaZhao/InfoVAE