Generated Data With Sparse Regularized Multi Pseudo Label for Person Re Identification
Recently, Generative Adversarial Network (GAN) has been adopted to improve person re identification (person re ID) performance through data augmentation. However, directly leveraging generated data to train a re ID model may easily lead to over fitting issue on these extra data and decrease the generalisability of model to learn true ID related features from real data. Inspired by the previous approach which assigns multi pseudo labels on the generated data to reduce the risk of over fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi Pseudo Label (SRMpL) can effectively prevent the over fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re ID datasets (e.g. Market 1501 and DukeMTMC reID) Compared with existing unlabeled generated data re ID solutions, our approach achieves competitive performance. Two classical re ID models are used to verify our sparse regularization label on generated data, i.e. an ID embedding network and a two stream network.
IEEE Signal Processing Letters