  Since human-labeled samples are free for the target set, unsupervised person
re-identification (Re-ID) has attracted much attention in recent years, by
additionally exploiting the source set. However, due to the differences on
camera styles, illumination and backgrounds, there exists a large gap between
source domain and target domain, introducing a great challenge on cross-domain
matching. To tackle this problem, in this paper we propose a novel method named
Dual-stream Reciprocal Disentanglement Learning (DRDL), which is quite
efficient in learning domain-invariant features. In DRDL, two encoders are
first constructed for id-related and id-unrelated feature extractions, which
are respectively measured by their associated classifiers. Furthermore,
followed by an adversarial learning strategy, both streams reciprocally and
positively effect each other, so that the id-related features and id-unrelated
features are completely disentangled from a given image, allowing the encoder
to be powerful enough to obtain the discriminative but domain-invariant
features. In contrast to existing approaches, our proposed method is free from
image generation, which not only reduces the computational complexity
remarkably, but also removes redundant information from id-related features.
Extensive experiments substantiate the superiority of our proposed method
compared with the state-of-the-arts. The source code has been released in
https://github.com/lhf12278/DRDL.
