论文标题
生成建模的公平性
Fairness in generative modeling
论文作者
论文摘要
我们设计了通用算法，以解决生成建模中的公平性问题和模式崩溃。更确切地说，要为尽可能多的敏感变量设计公平的算法，包括我们可能不知道的变量，我们不假定对敏感变量的先验知识：我们的算法仅使用无监督的公平性，这意味着没有与敏感变量相关的信息用于我们的公平性强度 - 改善方法。所有面孔的图像（甚至是生成的图像）都已删除以减轻法律风险。
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variables as possible, including variables we might not be aware of, we assume no prior knowledge of sensitive variables: our algorithms use unsupervised fairness only, meaning no information related to the sensitive variables is used for our fairness-improving methods. All images of faces (even generated ones) have been removed to mitigate legal risks.
