# Learning Disentangled Representation for Robust Person Re-identification

arXiv: https://arxiv.org/abs/1910.12003 · HTML: https://mlsanity.com/paper/1910.12003/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 30 of 40)

- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [Video-based Person Re-identification with Spatial and Temporal Memory Networks](https://mlsanity.com/paper/2108.09039/) (2021)
- [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022)
- [Learning Domain Invariant Representations for Generalizable Person Re-Identification](https://mlsanity.com/paper/2103.15890/) (2021)
- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [DeepChange: A Large Long-Term Person Re-Identification Benchmark with Clothes Change](https://mlsanity.com/paper/2105.14685/) (2021)
- [From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization](https://mlsanity.com/paper/2503.00938/) (2025)
- [Scaling-up Disentanglement for Image Translation](https://mlsanity.com/paper/2103.14017/) (2021)
- [Video Action Recognition with Attentive Semantic Units](https://mlsanity.com/paper/2303.09756/) (2023)
- [Exploring the Quality of GAN Generated Images for Person Re-Identification](https://mlsanity.com/paper/2108.09977/) (2021)
- [DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID](https://mlsanity.com/paper/2411.07205/) (2024)
- [Learning Invariance from Generated Variance for Unsupervised Person Re-identification](https://mlsanity.com/paper/2301.00725/) (2023)
- [Exploring Invariant Representation for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2302.00884/) (2023)
- [CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification](https://mlsanity.com/paper/2410.11255/) (2024)
- [Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification](https://mlsanity.com/paper/2303.10976/) (2023)
- [Multi-modal Multi-platform Person Re-Identification: Benchmark and Method](https://mlsanity.com/paper/2503.17096/) (2025)
- [Label Distribution Learning for Generalizable Multi-source Person Re-identification](https://mlsanity.com/paper/2204.05903/) (2022)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)
- [Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization](https://mlsanity.com/paper/2112.03163/) (2021)
- [T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up](https://mlsanity.com/paper/2208.12752/) (2022)
- [Entity Aware Modelling: A Survey](https://mlsanity.com/paper/2302.08406/) (2023)
- [Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching](https://mlsanity.com/paper/2506.12413/) (2025)
- [DisenQ: Disentangling Q-Former for Activity-Biometrics](https://mlsanity.com/paper/2507.07262/) (2025)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [DiCo: Disentangled Concept Representation for Text-to-image Person Re-identification](https://mlsanity.com/paper/2601.10053/) (2026)
- [Going Deeper into Semi-supervised Person Re-identification](https://mlsanity.com/paper/2107.11566/) (2021)
- [Calibrated Feature Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2111.13945/) (2021)
- [Spectral Aware Softmax for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2302.01512/) (2023)
- [A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems](https://mlsanity.com/paper/2302.09119/) (2023)
- [Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary](https://mlsanity.com/paper/2304.06249/) (2023)

## Abstract (arXiv)

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose), which requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity-shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, significantly outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/.