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PyTorch Implementation of CVPR'19 (oral) - Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach
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README.md

MaxEnt-ARL: Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach

By Proteek Chandan Roy and Vishnu Naresh Boddeti

Introduction

This code archive includes the Python implementation of MaxEnt-ARL for mitigating leakage of sensitive information from learned image representations.

Citation

If you think MaxEnt-ARL is useful to your research, please cite:

@article{roy2019mitigating,
    title={Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach},
    author={Roy, Proteek Chandan and Boddeti, Vishnu Naresh},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    year={2019}
}

Usage

python main.py

By default this will run the experiment on CIFAR-100 dataset as described in the paper. Note that it will generate multiple runs of the trade-off between utility and privacy. The non-dominated solutions across the multiple runs provides the final trade-off front as reported in the paper.

In order to run the experiments on the other datasets in the paper, please edit the "main.py" file.

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