论文标题
通过基于样式的面部衰老增强面部数据多样性
Enhancing Facial Data Diversity with Style-based Face Aging
论文作者
论文摘要
培训公平分类器中的一个重要限制因素与数据集偏差的存在有关。特别是，面部数据集通常会偏向性别，年龄和种族等属性。如果没有缓解，偏见会导致对此类群体表现出不公平行为的算法。在这项工作中，我们解决了增加面部数据集在年龄方面的多样性的问题。具体而言，我们提出了一种基于新颖的，基于生成风格的架构，用于数据增强，该体系结构通过对多分辨率年龄歧视性表示的调节来捕获细粒度的老化模式。通过评估单个和跨数据库实验中的几个年龄通知的数据集，我们表明，所提出的方法的表现优于年龄转移的最先进算法，尤其是在标签分布尾部的年龄组的情况下。我们进一步显示了增强数据集中的多样性大大增加，根据既定指标的所有方法都优于所有方法。
A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. If not mitigated, bias leads to algorithms that exhibit unfair behaviour towards such groups. In this work, we address the problem of increasing the diversity of face datasets with respect to age. Concretely, we propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns by conditioning on multi-resolution age-discriminative representations. By evaluating on several age-annotated datasets in both single- and cross-database experiments, we show that the proposed method outperforms state-of-the-art algorithms for age transfer, especially in the case of age groups that lie in the tails of the label distribution. We further show significantly increased diversity in the augmented datasets, outperforming all compared methods according to established metrics.
