# Multi-pseudo Regularized Label for Generated Data in Person Re-Identification

arXiv: https://arxiv.org/abs/1801.06742 · HTML: https://mlsanity.com/paper/1801.06742/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2018.

## Built on by (most cited first, 12 of 12)

- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation](https://mlsanity.com/paper/2103.15685/) (2021)
- [Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption](https://mlsanity.com/paper/2203.00672/) (2022)
- [DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID](https://mlsanity.com/paper/2411.07205/) (2024)
- [T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up](https://mlsanity.com/paper/2208.12752/) (2022)
- [Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics](https://mlsanity.com/paper/2207.01204/) (2022)
- [CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation](https://mlsanity.com/paper/2402.14454/) (2024)
- [Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification](https://mlsanity.com/paper/2410.08460/) (2024)
- [Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification](https://mlsanity.com/paper/2410.08466/) (2024)
- [Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification](https://mlsanity.com/paper/2411.04255/) (2024)
- [Evolution of ReID: From Early Methods to LLM Integration](https://mlsanity.com/paper/2506.13039/) (2025)
- [Cross-Scenario Deraining Adaptation with Unpaired Data: Superpixel Structural Priors and Multi-Stage Pseudo-Rain Synthesis](https://mlsanity.com/paper/2603.21661/) (2026)

## Abstract (arXiv)

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images, the amount of available training data is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data. However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.