论文标题
功能多样性学习，带有无监督域自适应的人的样品辍学重新识别
Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification
论文作者
论文摘要
事实证明，基于聚类的方法在处理无监督的域自适应人员重新识别（REID）任务方面有效。但是，这种方法的现有作品仍然遭受嘈杂的伪标签和整个训练过程中不可靠的概括能力。为了解决这些问题，本文提出了一种新的方法，通过限制嘈杂的伪标签，以更好的概括能力来学习特征表示。首先，我们提出了一种样品辍学方法（SD）方法，以防止模型的训练掉入经常用嘈杂的伪标签的样品引起的恶性循环中。此外，我们提出了一种崭新的方法，该方法是针对经典相互教学结构下的特征多样性学习（FDL）的，它可以显着提高目标域上功能表示的概括能力。实验结果表明，我们提出的FDL-SD在多个基准数据集上实现了最先进的性能。
Clustering-based approach has proved effective in dealing with unsupervised domain adaptive person re-identification (ReID) tasks. However, existing works along this approach still suffer from noisy pseudo labels and the unreliable generalization ability during the whole training process. To solve these problems, this paper proposes a new approach to learn the feature representation with better generalization ability through limiting noisy pseudo labels. At first, we propose a Sample Dropout (SD) method to prevent the training of the model from falling into the vicious circle caused by samples that are frequently assigned with noisy pseudo labels. In addition, we put forward a brand-new method referred as to Feature Diversity Learning (FDL) under the classic mutual-teaching architecture, which can significantly improve the generalization ability of the feature representation on the target domain. Experimental results show that our proposed FDL-SD achieves the state-of-the-art performance on multiple benchmark datasets.
