Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model

TL;DR

This paper introduces a transfer learning framework for FairGANs using Variational Auto-Encoders, enabling the adaptation of fairness-aware generative models to new tasks while maintaining their core objectives.

Contribution

It presents a novel reprogramming approach that extends FairGANs with VAEs, improving transferability and applicability across different tasks.

Findings

Maintains data fairness and utility during transfer

Enhances model adaptability to new tasks

Discusses trade-offs and limitations of the approach

Abstract

Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment. Given the model's advantages and performance, we introduce a novel learning framework to transfer a pre-trained FairGAN to other tasks. This reprogramming process has the goal of maintaining the FairGAN's main targets of data utility, classification utility, and data fairness, while widening its applicability and ease of use. In this paper we present the technical extensions required to adapt the original architecture to this new framework (and in particular the use of Variational Auto-Encoders), and discuss the benefits, trade-offs, and limitations of the new model.