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.

翻译：无监督行人重识别通过单个神经网络的自我改进取得了巨大成功。然而，受限于判别信息多样性的缺失，单个网络在无监督条件下难以仅凭自身学习到充分的判别能力。为解决这一局限，我们提出了一种基于种群的演化博弈（PEG）框架，该框架通过迭代地进行选择、繁殖、变异和种群互学习来同时训练一组多样性丰富的神经网络。具体而言，将待保留网络的选择建模为合作博弈，并通过最优响应动力学求解；随后通过克隆和扰动网络超参数实现繁殖与变异以学习更多多样性；种群互学习则通过种群内网络间的知识蒸馏提升各网络的判别能力。此外，我们提出跨引用分散度（CRS）以近似评估无标签样本下的重识别模型性能，并将其作为PEG中网络选择的准则。CRS通过间接估计模型预测伪标签的准确性，根据特征空间的凝聚性与分离性来衡量模型性能。大量实验表明：（1）CRS可在无标签样本下近似衡量模型性能；（2）PEG在行人重识别任务中达到了新的最佳精度，充分展示了基于种群的网络协同训练在无监督学习中的巨大潜力。