# A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems

arXiv: https://arxiv.org/abs/2302.09119 · HTML: https://mlsanity.com/paper/2302.09119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019)
- [Pose-Normalized Image Generation for Person Re-identification](https://mlsanity.com/paper/1712.02225/) (2017)
- [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://mlsanity.com/paper/1910.10093/) (2019)
- [Learning Disentangled Representation for Robust Person Re-identification](https://mlsanity.com/paper/1910.12003/) (2019)
- [PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification](https://mlsanity.com/paper/1906.01792/) (2019)
- [M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification](https://mlsanity.com/paper/1811.03768/) (2018)
- [GAN-based Pose-aware Regulation for Video-based Person Re-identification](https://mlsanity.com/paper/1903.11552/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data Augmentation in Human-Centric Vision](https://mlsanity.com/paper/2403.08650/) (2024)

## Abstract (arXiv)

Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different cameras, it is currently an unsolved problem. In machine learning-based computer vision applications with reduced data sets, one possibility to improve the performance of re-identification system is through the augmentation of the set of images or videos available for training the neural models. Currently, one of the most robust ways to generate synthetic information for data augmentation, whether it is video, images or text, are the generative adversarial networks. This article reviews the most relevant recent approaches to improve the performance of person re-identification models through data augmentation, using generative adversarial networks. We focus on three categories of data augmentation approaches: style transfer, pose transfer, and random generation.