  Domain adaptive person re-identification (re-ID) is a challenging task,
especially when person identities in target domains are unknown. Existing
methods attempt to address this challenge by transferring image styles or
aligning feature distributions across domains, whereas the rich unlabeled
samples in target domains are not sufficiently exploited. This paper presents a
novel augmented discriminative clustering (AD-Cluster) technique that estimates
and augments person clusters in target domains and enforces the discrimination
ability of re-ID models with the augmented clusters. AD-Cluster is trained by
iterative density-based clustering, adaptive sample augmentation, and
discriminative feature learning. It learns an image generator and a feature
encoder which aim to maximize the intra-cluster diversity in the sample space
and minimize the intra-cluster distance in the feature space in an adversarial
min-max manner. Finally, AD-Cluster increases the diversity of sample clusters
and improves the discrimination capability of re-ID models greatly. Extensive
experiments over Market-1501 and DukeMTMC-reID show that AD-Cluster outperforms
the state-of-the-art with large margins.
