In this work, we propose to address the unsupervised domain adaptive (UDA) person re-id problem in which the model learns from an unlabeled target domain using a fully annotated source domain. Current approaches mainly address domain shift problem or the inter/intra-domain variation of the two domains. However, they have neglected to integrate the-easy-to-learn label distribution of the target domain into the model to improve its performance. Moreover, the automatic label assignment for the unlabeled target data currently used in UDA methods does not reflect the underlying data. To address these issues, we introduce a technique that enforces three properties: (1) target instance invariance that considers the target data and uses a key–value memory to guess the label distribution that is later used as the supervision signal. (2) a camera invariance, formed by unlabeled target images, and their camera-style transferred. Here, a new loss function is proposed to control overconfident predictions on the styled images. Lastly, (3) a hierarchical clustering-based optimization technique that exploits the similarities between the target images to constrain the supervision information of the first property. Here, we randomly allocate each target image to a separate cluster, then gradually incorporate similarity within each identity as we group similar images into clusters and use the cluster-IDs as the new target labels. We iteratively refine the guessed label distribution of the target domain by making predictions on the unlabeled target domain and then train the network with these new samples. Extensive experimental results on the concurrent use of these three properties demonstrate that the proposed model can achieve the state-of-the-art on unsupervised domain adaptive person re-id. Our work is important for knowledge discovery and knowledge transfer.