/Interpretable-Neural-Clustering

Code for the paper "XAI Beyond Classification: Interpretable Neural Clustering" (JMLR 2022)

Primary LanguagePython

Interpretable-Neural-Clustering (TELL)

This is the code for the paper "XAI Beyond Classification: Interpretable Neural Clustering" (JMLR 2022)

Usage

To train the model on the MNIST dataset, run

python main.py

The performance of the model is evaluated during the training process, together with the the visualization of the reconstructed cluster centers.

To perform tSNE visualization, run

python tSNE.py

Citation

If you find TELL useful in your research, please consider citing:

@article{peng2022xai,
  title={XAI Beyond Classification: Interpretable Neural Clustering},
  author={Peng, Xi and Li, Yunfan and Tsang, Ivor W and Zhu, Hongyuan and Lv, Jiancheng and Zhou, Joey Tianyi},
  journal={Journal of Machine Learning Research},
  volume={23},
  number={6},
  pages={1--28},
  year={2022}
}