Feature diversity learning with sample dropout for unsupervised domain adaptive person re-identification

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 representation ability during the whole training process. In order to solve these problems, in this paper, we propose 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, our method can correct the noisy labels and boost the representation ability. In addition, we put forward a 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 in an unsupervised fashion. Experimental results show that our proposed FDL-SD achieves the state-of-the-art performance on multiple well-known benchmark datasets.