Generative Moment Matching Networks(GMMNs)

This repo contains an implementation of Generative Moment Matching Networks(see https://arxiv.org/pdf/1502.02761.pdf) in pytorch(0.4)

Usage:

Clone the repo and change into the code directory. Since the pretrained weights are available, the outputs can be visualized using the command:
python visualize.py --vis=gmmn

The autoencoder output can also be visualized using a different argument:
python visualize.py --vis=autoencoder

In case the netowrks are to be trained from scratch, delete the models directory and run python train.py

Inferences:

Some conclusions drawn from this implementation are listed below:

• The network is very sensitive to network architecture and learning rate, Slight alterations in hyperparameters can affect results drastically.
• Increasing noise dimensionality(input to GMMN) adversely affects the quality of image generated.

Note:

The implementation uses MNIST dataset and differs from the algorithm proposed in the paper in the following ways:

• The autoencoders are not trained layerwise and then fine tuned, all layers of autoencoder are trained simultaneoulsy using MSE loss and Adam optimizer.
• Batch size is same for both autoencoder and GMMN.
• No dropout was used while training autoencoder.

References:

https://github.com/siddharth-agrawal/Generative-Moment-Matching-Networks