Semi-supervised deep embedded clustering (SDEC)

Keras implementation for our paper:

• Ren Y, Hu K, Dai X, et al. Semi-supervised deep embedded clustering[J]. Neurocomputing, 2019, 325: 121-130.

Usage

1. Install Keras v2.0, scikit-learn and git
   sudo pip install keras scikit-learn
sudo apt-get install git

2. Clone the code to local.
   git clone https://github.com/hkrds1996/SDEC.git SDEC

3. Get pre-trained autoencoder's weights.
   Follow instructions at https://github.com/piiswrong/dec to pre-train the autoencoder. Then save the trained weights to a keras model (e.g. mnist_ae_weights.h5) and put it in folder 'ae_weights'.
   If you do not want to install Caffe package, you can download the pretrained weights from
   https://github.com/hkrds1996/data_weights
   The put the ae_weights file to the dir of SDEC

4. Run experiment on MNIST.
   python SDEC.py mnist python IDEC.py mnist or python DEC.py mnist

The SDEC (DEC or iDEC) model is saved to "results/sdec_dataset:datasetgamma:number/SDEC_model_final.h5" ("results/dec_dataset:dataset/DEC_model_final.h5" or "results/idec_dataset:datasetgamma:number/IDEC_model_final.h5").

6. Run experiment on USPS, STL, or CIFAR_10.
   python SDEC.py datasetname
python IDEC.py datasetname
   or python DEC.py datasetname

Models

The SDEC model:

The IDEC model:

The DEC model: