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

Understand

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.