  The challenge of unsupervised person re-identification (ReID) lies in
learning discriminative features without true labels. This paper formulates
unsupervised person ReID as a multi-label classification task to progressively
seek true labels. Our method starts by assigning each person image with a
single-class label, then evolves to multi-label classification by leveraging
the updated ReID model for label prediction. The label prediction comprises
similarity computation and cycle consistency to ensure the quality of predicted
labels. To boost the ReID model training efficiency in multi-label
classification, we further propose the memory-based multi-label classification
loss (MMCL). MMCL works with memory-based non-parametric classifier and
integrates multi-label classification and single-label classification in a
unified framework. Our label prediction and MMCL work iteratively and
substantially boost the ReID performance. Experiments on several large-scale
person ReID datasets demonstrate the superiority of our method in unsupervised
person ReID. Our method also allows to use labeled person images in other
domains. Under this transfer learning setting, our method also achieves
state-of-the-art performance.
