平衡人脸数据集：引导 StyleGAN 为代表性不足群体生成带标签的合成人脸图像数据集
Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group
AI总结：
本文提出通过控制 StyleGAN 生成过程创建平衡的合成人脸数据集，以缓解代表性不足群体带来的机器学习偏见，并为下游任务提供标注。
摘要：
为了让机器学习模型在特定问题域中对未见数据有效泛化，人们普遍理解数据需要具有足够的规模并能够代表真实世界场景。然而，真实世界数据集经常存在群体被过度代表和代表性不足的问题。缓解机器学习偏见的一种方案是利用多样且具有代表性的数据集。在覆盖所有人口统计群体的数据集上训练模型，对于减少机器学习中的偏见至关重要。但是，大规模数据集的收集和标注一直具有挑战性，这促使人们使用合成数据生成和主动标注来降低人工标注成本。本研究的重点是使用 StyleGAN 模型生成一个鲁棒的人脸图像数据集。为了实现数据集在不同人口统计群体之间的平衡分布，通过控制 StyleGAN 的生成过程创建了一个合成数据集，并为不同的下游任务进行了标注。
英文摘要：
For a machine learning model to generalize effectively to unseen data within a particular problem domain, it is well-understood that the data needs to be of sufficient size and representative of real-world scenarios. Nonetheless, real-world datasets frequently have overrepresented and underrepresented groups. One solution to mitigate bias in machine learning is to leverage a diverse and representative dataset. Training a model on a dataset that covers all demographics is crucial to reducing bias in machine learning. However, collecting and labeling large-scale datasets has been challenging, prompting the use of synthetic data generation and active labeling to decrease the costs of manual labeling. The focus of this study was to generate a robust face image dataset using the StyleGAN model. In order to achieve a balanced distribution of the dataset among different demographic groups, a synthetic dataset was created by controlling the generation process of StyleGaN and annotated for different downstream tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
