ClusterGAN

Code for reproducing key results in the paper ClusterGAN : Latent Space Clustering in Generative Adversarial Networks by Sudipto Mukherjee, Himanshu Asnani, Eugene Lin and Sreeram Kannan. If you use the code, please cite our paper.

Dependencies

The code has been tested with the following versions of packages.

• Python 2.7.12
• Tensorflow 1.4.0
• Numpy 1.14.2

Datasets

The datasets used in the paper can be downloaded from the Google Drive link (https://drive.google.com/open?id=1XnGkSamF5DiwnpHFG0OexmoqAwe27ucR). Unzip the folder so that the path is : ./ClusterGAN/data/<dataset_name>

Training

You can either train your own models on the datasets or use pre-trained models. Even though we have used a fixed seed using tf.random.seed(0), there will still be randomness introduced by CUDA. So, to reproduce the results, train 5 models and compare the Validation purity in the logs directory. Each model can be trained as follows :

$ python Image_Cluster.py --data mnist --K 10 --dz 30 --beta_n 10 --beta_c 10 --train True 

This will save the model along with timestamp in checkpoint-dir/<dataset_name>. Also, the Validation set performance will be written to logs/Res_<dataset_name>_<model_name>.txt. Then run the best model (with highest Validation Purity) on the Test set.

$ python Image_ClusterGAN.py --data mnist --K 10 --dz 30 --beta_n 10 --beta_c 10 --timestamp <best_timestamp>

Training the models for other datasets has a similar format.

Fashion-10 :

$ python Image_ClusterGAN.py --data fashion --K 10 --dz 40 --beta_n 0 --beta_c 10 --train True 

Fashion-5 :

$ python Image_ClusterGAN.py --data fashion --K 5 --dz 40 --beta_n 0 --beta_c 10 --train True 

Single Cell 10x genomics :

$ python Gene_ClusterGAN.py --data 10x_73k --K 8 --dz 30 --beta_n 10 --beta_c 10 --train True 

Pendigits :

$ python pen_ClusterGAN.py --data pendigit --K 10 --dz 5 --beta_n 10 --beta_c 10 --train True 

Provide the timestamp of best saved model to obtain the Test set clustering performance on all the datasets (similar to MNIST above).

Pre-trained models

Run the following code :

$ python Image_ClusterGAN.py --data mnist --K 10 --dz 30 --beta_n 10 --beta_c 10 

Similarly for the other datasets.

Clustering Performance

Table shows the mean +- standard deviation of 10 runs of ClusterGAN (with the reported hyperparameter settings in the paper) for various datasets.

Dataset	ACC	NMI	ARI
MNIST	0.9097 +- 0.0398	0.8544 +- 0.0361	0.8290 +- 0.0621
Fashion-10	0.6119 +- 0.0230	0.6157 +- 0.0112	0.4617 +- 0.0226
Fashion-5	0.7218 +- 0.0089	0.6163 +- 0.0243	0.5035 +- 0.0228
10x_73k	0.8172 +- 0.0262	0.7272 +- 0.0322	0.6786 +- 0.0369
Pendigits	0.7638 +- 0.0120	0.7343 +- 0.0120	0.6336 +- 0.0177

Feedback

Please feel free to provide any feedback about the code to sudipto.ece.ju@gmail.com