Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

• URL: http://arxiv.org/abs/2306.05236v1
• Date: Thu, 8 Jun 2023 14:33:41 GMT
• Title: Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
• Authors: Yunpeng Zhai, Peixi Peng, Mengxi Jia, Shiyong Li, Weiqiang Chen, Xuesong Gao, Yonghong Tian
• Abstract summary: Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. 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. 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.
• Score: 26.279581599246224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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