论文标题
联合无监督的领域适应面部识别
Federated Unsupervised Domain Adaptation for Face Recognition
论文作者
论文摘要
给定的源域中标记的数据，无监督的域适应已被广泛采用，以概括目标域中未标记数据的模型，该数据分布不同。但是，在隐私约束下，现有作品无法面对识别，因为它们需要在域之间共享敏感的面部图像。为了解决这个问题，我们提出了联合无监督的领域适应面部识别，FedFr。 FEDFR共同优化了基于聚类的域的适应性和联合学习，以提高目标域的性能。具体而言，对于目标域中的未标记数据，我们增强了具有距离约束的聚类算法以提高预测伪标签的质量。此外，我们提出了一个新的域约束损失（DCL），以使联邦学习中的源域培训正常。在新建造的基准上进行的广泛实验表明，FEDFR在不同评估指标上的基线和经典方法的表现高3％至14％。
Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions are different. However, existing works are inapplicable to face recognition under privacy constraints because they require sharing of sensitive face images between domains. To address this problem, we propose federated unsupervised domain adaptation for face recognition, FedFR. FedFR jointly optimizes clustering-based domain adaptation and federated learning to elevate performance on the target domain. Specifically, for unlabeled data in the target domain, we enhance a clustering algorithm with distance constrain to improve the quality of predicted pseudo labels. Besides, we propose a new domain constraint loss (DCL) to regularize source domain training in federated learning. Extensive experiments on a newly constructed benchmark demonstrate that FedFR outperforms the baseline and classic methods on the target domain by 3% to 14% on different evaluation metrics.
