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.
The primary experiments and code are presented in an Jupyter Notebook, iclr_har_reproduced.ipynb. The notebook requires
matplotlib to run.
We also attempted to apply the model to a novel dataset, the UCI Adult dataset, with limited results in iclr_har_reproduced.ipynb.
|Angel Alexander Cabrera||Georgia Tech|
|Varun Gupta||Georgia Tech|
|Will Epperson||Georgia Tech|