Code for reproducing key results in the paper Clustering by Directly Disentangling Latent Space by Fei Ding and Feng Luo. If you use the code, please cite our paper.

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

• Python 3
• Tensorflow 1.14.0
• Numpy 1.14.2

The datasets used in the paper can be downloaded from the Google Drive link (https://drive.google.com/open?id=1XnGkSamF5DiwnpHFG0OexmoqAwe27ucR) and (https://drive.google.com/drive/folders/0B9J-9A2jotGRT25vSDhUWTQxVWs).

Unzip the folder so that the path is : ./ClusterGAN/data/<dataset_name>

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 25 --beta_n 1 --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_Cluster.py --data mnist --K 10 --dz 25 --beta_n 1 --beta_c 10 --timestamp <best_timestamp>

Training the models for other datasets has a similar format.

Fashion-10 :

$ python Image_Cluster.py --data fashion --K 10 --dz 40 --beta_n 1 --beta_c 10 --train True 

YTF :

$ python Image_Cluster.py --data ytf --K 41 --dz 60 --beta_n 1 --beta_c 10 --train True 

Coil-100 :

$ python Image_Cluster.py --data coil --K 100 --dz 100 --beta_n 1 --beta_c 10 --train True 

Single Cell 10x genomics :

$ python Seq_Cluster.py --data 10x_73k --K 8 --dz 30 --beta_n 1 --beta_c 10 --train True 

Pendigits :

$ python Pen_Cluster.py --data pendigit --K 10 --dz 5 --beta_n 1 --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).

Additionally, you can also download the pre-trained models from the Google drive link (https://drive.google.com/open?id=1l9Lwq0amAaA3qHzNCiw7BrivSAFoP0em). Unzip the file in ./ClusterGAN. It should lead to the folder ./ClusterGAN/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.

• https://github.com/sudiptodip15/ClusterGAN
• https://github.com/tolstikhin/wae