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A PID controller method for meta-learning about aerospace target classification

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Abstract

Traditional deep learning algorithms cannot accomplish few-shot aerospace targets classification task because the aerospace target classification dataset is scarcity. Meta-learning has strong generalization ability and quickly learn new tasks based on the acquisition of existing knowledge, which has greater advantages than traditional machine learning to solve few-shot aerospace targets classification task. Model-agnostic meta-learning (MAML) was the most classical optimization based meta-learning algorithm, while not easy to converge, and prone to oscillate at the end of training. Thus, we proposed a PID based meta-learning algorithm, and named as PIDMAML, which has good generalization and stable convergence. In the PIDMAML algorithm, the update of the model not only depends on the query set gradient (MAML), but the integration and differential of the support set gradient and query set gradient, which can retain more information on each task, and obtain a better initial model. The integration and differential of the support set gradient and query set gradient can accelerate model convergence and can apply to other MAML-based method. We also proposed an aerospace target dataset for few-shot aerospace target classification task. We verified the effectiveness of the PIDMAML algorithm on the Omniglot and MiniImagenet dataset, and the versatility of the PIDMAML on the Aerospace dataset. PIDMAML obtain much better results than classical meta-learning method.

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Data availability statement

The Aerospace datasets generated for our paper can be requested from the corresponding author.

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Acknowledgements

This work was supported in part by the National Key R &D Program of China (No. 2019YFB1312001)

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Authors and Affiliations

  1. Department of control science and Engineering, Harbin Institute of Technology, Xidazhijie, Harbin, 150001, Heilongjiang Province, China

    Zhongliang Yu & Jianfeng Lv

  2. The College of Automation, Chongqing University, No.174, Shazhengjie, Shapingba, Chongqing, 400044, China

    Zhongliang Yu

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  1. Zhongliang Yu
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Correspondence to Zhongliang Yu.

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Yu, Z., Lv, J. A PID controller method for meta-learning about aerospace target classification. Multimed Tools Appl 83, 39217–39233 (2024). https://doi.org/10.1007/s11042-023-17022-0

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