  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/.
