Semi-supervised learning for person re-identification based on style-transfer-generated data by CycleGANs

Person re-identification (re-ID) is an exceedingly significant branch in the field of computer vision, especially for video surveillance. It is still a challenge to obtain more labeled training data and use them reasonably for more precise matching, though the person re-ID performance has been improved significantly. In order to solve this challenge, this study proposes a semi-supervised learning algorithm for data augmentation, the style-transfer-generated data as an extra class (STGDEC), which is aided by the Cycle-Consistent Adversarial Networks (CycleGANs) in generating extra unlabeled training data. Specifically, the algorithm firstly trains the CycleGANs and Deep Convolutional Generative Adversarial Networks so as to generate large amounts of unlabeled data. Secondly, we propose an adaptive receptive field module to expand the size of receptive fields and select the appropriate receptive field features dynamically in order to learn more contextual information and discriminative feature representation and embed the module in the backbone network easily. Thirdly, we use the combination of label smoothing regularization for outliers and an extra class loss to regularize the generated data and encourage the network not to be too confident to the ground-truth. Finally, this paper proposes three training strategies for the combination of standard dataset and generated samples. Comprehensive experiments based on the STGDEC are conducted, and these results show that the proposed algorithm gains a significant improvement over the baseline, the Basel. + LSRO and state-of-the-art approaches of person re-ID in many cases.