Related papers: Improving Fairness and Mitigating MADness in Generative Models

Improving Fairness and Mitigating MADness in Generative Models

URL: http://arxiv.org/abs/2405.13977v3
Date: Thu, 03 Oct 2024 21:46:36 GMT
Title: Improving Fairness and Mitigating MADness in Generative Models
Authors: Paul Mayer, Lorenzo Luzi, Ali Siahkoohi, Don H. Johnson, Richard G. Baraniuk,
Abstract summary: We show that training generative models with intentionally designed hypernetworks leads to models that are more fair when generating datapoints belonging to minority classes. We introduce a regularization term that penalizes discrepancies between a generative model's estimated weights when trained on real data versus its own synthetic data.
Score: 21.024727486615646
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Abstract: Generative models unfairly penalize data belonging to minority classes, suffer from model autophagy disorder (MADness), and learn biased estimates of the underlying distribution parameters. Our theoretical and empirical results show that training generative models with intentionally designed hypernetworks leads to models that 1) are more fair when generating datapoints belonging to minority classes 2) are more stable in a self-consumed (i.e., MAD) setting, and 3) learn parameters that are less statistically biased. To further mitigate unfairness, MADness, and bias, we introduce a regularization term that penalizes discrepancies between a generative model's estimated weights when trained on real data versus its own synthetic data. To facilitate training existing deep generative models within our framework, we offer a scalable implementation of hypernetworks that automatically generates a hypernetwork architecture for any given generative model.

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