Population-Based Evolutionary Gaming for Unsupervised Person
  Re-identification
Yunpeng Zhai Peixi Peng Mengxi Jia Shiyong Li Weiqiang Chen Xuesong Gao Yonghong Tian
Unsupervised person re-identification has achieved great success through the
self-improvement of individual neural networks. However, limited by the lack of
diversity of discriminant information, a single network has difficulty learning
sufficient discrimination ability by itself under unsupervised conditions. To
address this limit, we develop a population-based evolutionary gaming (PEG)
framework in which a population of diverse neural networks is trained
concurrently through selection, reproduction, mutation, and population mutual
learning iteratively. Specifically, the selection of networks to preserve is
modeled as a cooperative game and solved by the best-response dynamics, then
the reproduction and mutation are implemented by cloning and fluctuating
hyper-parameters of networks to learn more diversity, and population mutual
learning improves the discrimination of networks by knowledge distillation from
each other within the population. In addition, we propose a cross-reference
scatter (CRS) to approximately evaluate re-ID models without labeled samples
and adopt it as the criterion of network selection in PEG. CRS measures a
model's performance by indirectly estimating the accuracy of its predicted
pseudo-labels according to the cohesion and separation of the feature space.
Extensive experiments demonstrate that (1) CRS approximately measures the
performance of models without labeled samples; (2) and PEG produces new
state-of-the-art accuracy for person re-identification, indicating the great
potential of population-based network cooperative training for unsupervised
learning.