# CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification

arXiv: https://arxiv.org/abs/1904.01308 · HTML: https://mlsanity.com/paper/1904.01308/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Online Unsupervised Domain Adaptation for Person Re-identification](https://mlsanity.com/paper/2205.04383/) (2022)
- [Source-Guided Similarity Preservation for Online Person Re-Identification](https://mlsanity.com/paper/2402.15206/) (2024)
- [Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification](https://mlsanity.com/paper/2306.03993/) (2023)
- [Privacy-Preserving Adaptive Re-Identification without Image Transfer](https://mlsanity.com/paper/2407.12589/) (2024)

## Abstract (arXiv)

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model will be made publicly available at https://team.inria.fr/perception/canu-reid/.