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AR-DMVC-AM

Official implementation of the ICML'24 paper "Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework".

Preparing datasets

The multi-view dataset NoisyMNIST, NoisyFashion, and PatchedMNIST can be generated by running

python -m data.make_dataset noisymnist noisyfashionmnist patchedmnist

The RegDB dataset can be obtained through paper.

Running experiments

To train the proposed AR-DMVC-AM or AR-DMVC on the provided dataset, e.g., NoisyMNIST, execute:

python run.py --model_name ardmvc_am --data_name noisymnist

or

python run.py --model_name ardmvc --data_name noisymnist

To experiment with other deep multi-view clustering methods, run:

python run_other.py --model_name <model name> --data_name noisymnist

where <model name> refers to the name of deep multi-view models in the program, as shown in the table below compared to the article:

Name in paperName in program
EAMCeamc
SiMVCsimvc
CoMVCcomvc
Multi-VAEmvae
AECoDDCcae
InfoDDCmimvc
SEMSEM

Printing images

To print the adversarial samples of different deep multi-view models after running the experiment, run:

python atk_plot.py --model_name ardmvc_am --data_name noisymnist

The images will all be saved in atk_plot.

Citation

If you think our work is useful, please consider citing:

@inproceedings{huang2024adversarially,
title={Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework},
author={Huang, Haonan and Zhou, Guoxu and Zheng, Yanghang and Qiu, Yuning and Wang, Andong and Zhao, Qibin},
booktitle={International Conference on Machine Learning},
year={2024},
organization={PMLR}
}

Contact

If you have any questions or feedback, please feel free to contact us at libertyhhn@foxmail.com (Haonan Huang, GDUT) or illusionzyh@foxmail.com (Yanghang Zheng, GDUT).