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This directory is for simulating FR-Train 
[, ICML 2020] on synthetic dataset.
The program needs PyTorch and Jupyter Notebook.

The directory contains total 8 files: 1 README, 1 python file, 
2 jupyter notebooks, and 4 data files (3 numpy files for synthetic data, 
1 text file for poisoning index)
To simulate FR-Train, please use the jupyter notebooks in the directory.

FRTrain_clean.ipynb and FRTrain_poisoned.ipynb contain clean mode and
poisoned mode, respectively.
The jupyter notebooks will load the data and put the arranged dataset 
into train_model(). The variable 'y_train' contains different data 
depending on whether it is a clean or poisoned mode.

The train_model() will train FR-Train by using the classes in
After the training, train_model() will return the test accuracy and
disparate impact to the caller.

The python file is contains the defined structures 
of FR-Train: generator and two discriminators (for fairness and 
robustness each). 

The detailed explanations about each component have been written 
in the codes as comments.


FR-Train: A Mutual Information-Based Approach to Fair and Robust Training



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