论文标题
通过潜在空间偏见的公平属性分类
Fair Attribute Classification through Latent Space De-biasing
论文作者
论文摘要
视觉识别的公平性正成为一个著名和关键的讨论主题，因为在现实世界中大规模部署了识别系统。已知从目标标签与受保护属性（例如性别，种族）相关的数据训练的模型已知可以学习和利用这些相关性。在这项工作中，我们引入了一种训练准确目标分类器的方法，同时减轻源于这些相关性的偏见。我们使用gans生成逼真的图像，并将这些图像在潜在的潜在空间中扰动，以生成为每个受保护属性平衡的训练数据。我们使用这种扰动的生成数据来增强原始数据集，并在经验上证明，在增强数据集中训练的目标分类器表现出许多定量和定性的好处。我们对Celeba数据集中的多个目标标签和受保护的属性进行了彻底的评估，并提供了与该领域现有文献的深入分析和比较。
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.
