# Deep Transfer Learning for Person Re-identification

arXiv: https://arxiv.org/abs/1611.05244 · HTML: https://mlsanity.com/paper/1611.05244/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2016.

## Built on by (most cited first, 16 of 16)

- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Consistency and Diversity induced Human Motion Segmentation](https://mlsanity.com/paper/2202.04861/) (2022)
- [CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification](https://mlsanity.com/paper/2410.09382/) (2024)
- [Pose Invariant Person Re-Identification using Robust Pose-transformation GAN](https://mlsanity.com/paper/2105.00930/) (2021)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [Tran-GCN: A Transformer-Enhanced Graph Convolutional Network for Person Re-Identification in Monitoring Videos](https://mlsanity.com/paper/2409.09391/) (2024)
- [Global-Local Dynamic Feature Alignment Network for Person Re-Identification](https://mlsanity.com/paper/2109.05759/) (2021)
- [A Little Bit Attention Is All You Need for Person Re-Identification](https://mlsanity.com/paper/2302.14574/) (2023)
- [Quantum learning and essential cognition under the traction of meta-characteristics in an open world](https://mlsanity.com/paper/2311.13335/) (2023)
- [Attribute Guided Sparse Tensor-Based Model for Person Re-Identification](https://mlsanity.com/paper/2108.04352/) (2021)
- [EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning](https://mlsanity.com/paper/2205.10356/) (2022)
- [Benchmarking person re-identification datasets and approaches for practical real-world implementations](https://mlsanity.com/paper/2212.09981/) (2022)
- [CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion](https://mlsanity.com/paper/2508.04036/) (2025)
- [A review of Recent Techniques for Person Re-Identification](https://mlsanity.com/paper/2509.22690/) (2025)

## Abstract (arXiv)

Person re-identification (Re-ID) poses a unique challenge to deep learning: how to learn a deep model with millions of parameters on a small training set of few or no labels. In this paper, a number of deep transfer learning models are proposed to address the data sparsity problem. First, a deep network architecture is designed which differs from existing deep Re-ID models in that (a) it is more suitable for transferring representations learned from large image classification datasets, and (b) classification loss and verification loss are combined, each of which adopts a different dropout strategy. Second, a two-stepped fine-tuning strategy is developed to transfer knowledge from auxiliary datasets. Third, given an unlabelled Re-ID dataset, a novel unsupervised deep transfer learning model is developed based on co-training. The proposed models outperform the state-of-the-art deep Re-ID models by large margins: we achieve Rank-1 accuracy of 85.4%, 83.7% and 56.3% on CUHK03, Market1501, and VIPeR respectively, whilst on VIPeR, our unsupervised model (45.1%) beats most supervised models.