Sufficient training data is normally required to train deeply learned models. However, the number of pedestrian images per ID in person re-identification (re-ID) datasets is usually limited, since manually annotations are required for multiple camera views. To produce more data for training deeply learned models, generative adversarial network (GAN) can be leveraged to generate samples for person re-ID. However, the samples generated by vanilla GAN usually do not have labels. So in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated images. With MpRL, the generated samples will be used as supplementary of real training data to train a deep model in a semi-supervised learning fashion. Considering data bias between generated and real samples, MpRL utilizes different contributions from predefined training classes. The contribution-based virtual labels are automatically assigned to generated samples to reduce ambiguous prediction in training. Meanwhile, MpRL only relies on predefined training classes without using extra classes. Furthermore, to reduce over-fitting, a regularized manner is applied to MpRL to regularize the learning process. To verify the effectiveness of MpRL, two state-of-the-art convolutional neural networks (CNNs) are adopted in our experiments. Experiments demonstrate that by assigning MpRL to generated samples, we can further improve the person re-ID performance on three datasets i.e., Market-1501, DukeMTMCreID, and CUHK03. The proposed method obtains +6.29%, +6.30% and +5.58% improvements in rank-1 accuracy over a strong CNN baseline respectively, and outperforms the state-of-the- art methods.


翻译:通常需要足够的培训数据来培训深层学习模型。然而,通常需要有足够的培训数据来培训深层学习模型。然而,在本文中,我们提议一个虚拟标签,名为多普瑟多正规化的Label(MpRL),并将其分配给生成的图像。有了MpRL,所生成的样本将用作实际培训数据的补充,以在半监督的学习方式中培训深层模型。考虑到生成的和真实的样本之间的数据偏差,MpRL可以利用基因对抗网络(GAN)来生成样本。然而,香草GAN生成的样本通常没有标签。因此,我们在此文件中,我们提议一个名为多普瑟多级的Label(MpRRLL) 的虚拟标签,并且将它指定为预定义的Label6, 并分配给生成的图像。此外,由于MCNRLLL, 生成的样本将用作真实的精确度, 将MRR-R-R-l 3 的精确度用于测试过程。

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