Fair Attribute Classification through Latent Space De-biasing

TL;DR

This paper presents a novel method using GANs to generate and perturb images in latent space, creating balanced training data to reduce bias in visual recognition models.

Contribution

It introduces a latent space de-biasing technique with GANs to improve fairness in attribute classification without sacrificing accuracy.

Findings

Balanced training data reduces bias in classifiers.

Method improves fairness metrics across multiple attributes.

Enhanced qualitative and quantitative performance on CelebA.

Abstract

Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in which target labels are correlated with protected attributes (e.g., gender, race) are known to learn and exploit those correlations. In this work, we introduce a method for training accurate target classifiers while mitigating biases that stem from these correlations. We use GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute. We augment the original dataset with this perturbed generated data, and empirically demonstrate that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits. We conduct a thorough evaluation across multiple target labels and protected attributes in the CelebA dataset, and provide an in-depth analysis and comparison to existing literature in the space.