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ICLR Reproducibility Challenge: Generative Adversarial Models For Learning Private And Fair Representations

We present code reproducing some of the results for a paper submitted to ICLR'19 entitled Generative Adversarial Models for Learning Private and Fair Representations. This paper explores using a generative adversarial model as a decorrelation mechanism that hides a sensitive variable while still preserving the utility of the original data. In our work, we replicated the architecture described in the paper using PyTorch and show a successful use case for the Human Activity Recognition (HAR) dataset.

Repository Structure

The primary experiments and code are presented in an Jupyter Notebook, iclr_har_reproduced.ipynb. The notebook requires PyTorch, Pandas, numpy, and matplotlib to run.

This project was completed in part as a final project for the Georgia Tech class CS7643 Deep Learning. As part of our submission, we created a website explaining our process and results.

We also attempted to apply the model to a novel dataset, the UCI Adult dataset, with limited results in iclr_har_reproduced.ipynb.

Researchers

Links

• Original Paper Submission
• CS7643 Deep Learning
• Reproducibility Challenge
• GitHub Tracker