This is an implementation of the paper Generative Moment Matching Networks, ICML 2015. The paper can be found here: https://arxiv.org/abs/1502.02761. This implementation is in Python using Tensorflow.

Dependencies

The implementation depends on the following Python libraries: argparse, cPickle, math, matplotlib, numpy, random, tensorflow

Usage

1. Extract the data from data.tar.gz into the same folder as that of the implementation generativeMomentMatchingNetworks.py. The data contains two files mnist.pkl and lfw.npy, for the MNIST and LFW datasets respectively. The implementation uses LFW as the TFD (which is used in the paper) is not publicly available.
2. The implementation generativeMomentMatchingNetworks.py needs two command line arguments to work, the dataset (mnist, lfw) and the network to be used (data_space, code_space; more in the paper). These can be specified by the -d (or --dataset) and -n (or --network) respectively.

Example Usage: python generativeMomentMatchingNetworks.py -d mnist -n code_space

Results

Data Space

Code Space