Considering Race a Problem of Transfer Learning

Akbir Khan, M. Mahmoud
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引用次数: 8

Abstract

As biometric applications are fielded to serve large population groups, issues of performance differences between individual sub-groups are becoming increasingly important. In this paper we examine cases where we believe race is one such factor. We look in particular at two forms of problem; facial classification and image synthesis. We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets). We demonstrate a series of techniques to improve transfer learning of facial classification; outperforming similar models trained in the target's own domain. We conduct a study to evaluate the performance drop of Generative Adversarial Networks trained to conduct image synthesis, in this process, we produce a new annotation for the Celeb-A dataset by race. These networks are trained solely on one race and tested on another - demonstrating the subsets of the CelebA to be distinct domains for this task.
考虑种族问题的迁移学习
随着生物识别应用的应用范围越来越广,各个子群体之间的性能差异问题变得越来越重要。在本文中,我们研究了一些我们认为种族是其中一个因素的案例。我们特别关注两种形式的问题;人脸分类与图像合成。我们采用了一种新颖的方法,将种族作为任务(面部分类)和领域(不同数据集的综合)迁移学习的边界。我们展示了一系列改进面部分类迁移学习的技术;优于在目标领域训练的类似模型。我们进行了一项研究,以评估生成对抗网络训练进行图像合成的性能下降,在这个过程中,我们根据种族为名人- a数据集生成了一个新的注释。这些网络仅在一个种族上进行训练,并在另一个种族上进行测试-证明CelebA的子集是该任务的不同域。
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