论文标题
不完美的成像：剂量的含义加剧了对面部数据增强和Snapchat自拍镜头的偏见
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses
论文作者
论文摘要
在本文中，我们表明流行的生成对抗网络（gans）会加剧沿性别和肤色轴的偏见，当给出面部发射的偏斜分布时。虽然从业者使用gans庆祝合成数据作为增强培训数据渴望数据的机器学习模型的经济方式，但尚不清楚当将其应用于沿潜在维度偏见的现实世界数据集时，他们是否识别出这种技术的危险。具体而言，我们表明（1）传统剂量进一步划分了由工程教师爆头组成的数据集的分布，少数族裔模式的频率较低，质量较差，并且（2）图像到图像的翻译（条件）gans gans gans gans gans to toys的肤色也会使肤色降低肤色，并在女性面部效果上产生效果。因此，我们的研究旨在充当警示性故事。
In this paper, we show that popular Generative Adversarial Networks (GANs) exacerbate biases along the axes of gender and skin tone when given a skewed distribution of face-shots. While practitioners celebrate synthetic data generation using GANs as an economical way to augment data for training data-hungry machine learning models, it is unclear whether they recognize the perils of such techniques when applied to real world datasets biased along latent dimensions. Specifically, we show that (1) traditional GANs further skew the distribution of a dataset consisting of engineering faculty headshots, generating minority modes less often and of worse quality and (2) image-to-image translation (conditional) GANs also exacerbate biases by lightening skin color of non-white faces and transforming female facial features to be masculine when generating faces of engineering professors. Thus, our study is meant to serve as a cautionary tale.
