Learning Generic Feature Representations with Adversarial Regularization for Person Re-Identification

Many existing person re-identification (Re-ID) methods can achieve human-level accuracy on a single dataset, while most of them can be poorly generalized to other datasets. This is mainly caused by different data distributions between different domains. In this paper, we propose a novel adversarial regularization method to address this issue. Specifically, the features extracted from different datasets will be constrained and focused to follow a more similar distribution during the training process. As a result, our method can learn a feature representation with better inter-domain invariance, which will improve the generalization ability of the resulting model. Besides, our method is flexible and can be combined with any feature learning network. Extensive experiments on both Market1501 and DukeMTMC-reID datasets have demonstrated the effectiveness of our method.