  Due to the lack of labels and the domain diversities, it is a challenge to
study person re-identification in the cross-domain setting. An admirable method
is to optimize the target model by assigning pseudo-labels for unlabeled
samples through clustering. Usually, attributed to the domain gaps, the
pre-trained source domain model cannot extract appropriate target domain
features, which will dramatically affect the clustering performance and the
accuracy of pseudo-labels. Extensive label noise will lead to sub-optimal
solutions doubtlessly. To solve these problems, we propose an Anti-Noise
Learning (ANL) approach, which contains two modules. The Feature Distribution
Alignment (FDA) module is designed to gather the id-related samples and
disperse id-unrelated samples, through the camera-wise contrastive learning and
adversarial adaptation. Creating a friendly cross-feature foundation for
clustering that is to reduce clustering noise. Besides, the Reliable Sample
Selection (RSS) module utilizes an Auxiliary Model to correct noisy labels and
select reliable samples for the Main Model. In order to effectively utilize the
outlier information generated by the clustering algorithm and RSS module, we
train these samples at the instance-level. The experiments demonstrate that our
proposed ANL framework can effectively reduce the domain conflicts and
alleviate the influence of noisy samples, as well as superior performance
compared with the state-of-the-art methods.
