Jointly Learn the Base Clustering and Ensemble for Deep Image Clustering(JDCE)

This is the code for the paper "Jointly Learn the Base Clustering and Ensemble for Deep Image Clustering" (ICME 2024)

Dependency

• python==3.10.9
• numpy==1.24.2
• Pillow==10.3.0
• scikit_learn==1.1.1
• scipy==1.10.0
• torch==1.12.0
• torchvision==0.13.0
• tqdm==4.64.0

Datasets

CIFAR-10, CIFAR-100 and STL-10 can be automatically downloaded by Pytorch in folder data.

For ImageNet-10 and ImageNet-dogs, the description of selected subsets can find in folder data/dataset_name. The folder of these two ImageNet datasets should be like this:

data
  ├── ImageNet-10
  │     ├──data.npy
  │     ├──label.npy
  ├── ImageNet-DOG
  │     ├──data.npy
  │     ├──label.npy

Usage

Training

To train our model, run the following script.

$ python main.py

Test

Once the training is completed, there will be a saved model in folder saved_model/dataset_name. To test the trained model, run

$ python test.py

We provide some trained models, please download here.

Citation

@inproceedings{Chen_2024_Icme,
    author={Chen Liang, Zhiqian Dong, Sheng Yang, Peng Zhou},
    title={Jointly Learn the Base Clustering and Ensemble for Deep Image Clustering},
    booktitle={International Conference on Multimedia and Expo},
    year={2024},
}

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Credit

Some parts of this code (e.g., network) are based on CC repository.