论文标题
关于gan的有偏见以进行面部验证
On Biased Behavior of GANs for Face Verification
论文作者
论文摘要
深度学习系统需要大量数据进行培训。用于培训面部验证系统的数据集很难获得，并且容易困扰隐私问题。由GAN等生成模型生成的合成数据可以是一个很好的选择。但是，我们表明，从gan产生的数据容易出现偏见和公平问题。具体而言，接受过FFHQ数据集训练的甘恩（GAN）表明，在20-29岁年龄段的年龄组中产生白色面孔的行为有偏见。我们还证明，当用于微调面部验证系统时，合成面部面孔会引起不同的影响，特别是针对种族属性的影响。
Deep Learning systems need large data for training. Datasets for training face verification systems are difficult to obtain and prone to privacy issues. Synthetic data generated by generative models such as GANs can be a good alternative. However, we show that data generated from GANs are prone to bias and fairness issues. Specifically, GANs trained on FFHQ dataset show biased behavior towards generating white faces in the age group of 20-29. We also demonstrate that synthetic faces cause disparate impact, specifically for race attribute, when used for fine tuning face verification systems.
