Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data, where some spurious attributes (e.g., Male) statistically correlate with the target attribute (e.g., Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes, which may be unavailable in practice. This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially, we identify the potential spurious attributes based on generative models. Notably, it enhances interpretability by explicitly showing the spurious attributes in image space. Following this, for each image, we first edit the spurious attributes with a random degree sampled from a uniform distribution, while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA.

翻译：面部属性分类（FAC）在实际应用中具有广阔前景。然而，采用传统方法训练的FAC模型可能因不同数据子群体间的准确率差异而存在不公平性。这种不公平性主要源于数据中的偏差，即某些虚假属性（如“男性”）与目标属性（如“微笑”）存在统计相关性。现有公平性感知方法大多依赖虚假属性的标签，但在实践中这些标签可能难以获取。本文提出一种新颖的基于生成的两阶段框架，可在无需额外标注的情况下，基于有偏数据训练公平的FAC模型。首先，我们通过生成模型识别潜在的虚假属性。值得注意的是，该方法通过显式展示图像空间中的虚假属性增强了可解释性。随后，对于每张图像，我们首先保持目标属性不变，从均匀分布中采样随机程度来编辑虚假属性，进而通过增强模型对这些操作的鲁棒性来训练公平的FAC模型。在三个通用数据集上的大量实验表明，我们的方法在促进FAC公平性的同时能保持准确率不降低。代码已开源至https://github.com/heqianpei/DiGA。