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A Framework for Enhanced Adaptability and Interpretability in Meta-Learning
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Affiliation:

1.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology;2.Guangxi Key Laboratory of Image and Graphic intelligent processing, Guilin University of Electronic Technology;3.School of Information Engineering, Institute of Disaster Prevention

Fund Project:

The Key R&D Program of Guangxi under Grant(AB21220023) ；The National Natural Science Foundation of China (42164002); Guangxi Graduate Education Innovation Project(YCSW2023308)

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    Abstract:

    In few-shot learning scenarios, Model-Agnostic Meta-Learning (MAML) is renowned for its flexibility and applicability independent of specific base model. However, MAML and its variants lack the ability to adaptively adjust task-specific features, such as feature channels, and rely on global initial parameters with fixed inner-loop step numbers, resulting in a training process that lacks interpretability. This paper proposes a rapid, interpretable, and adaptive method using logistic regression and channel attention (ML-LRCA). The goal is to enhance the adaptability and interpretability of the framework by adaptively adjusting task-specific channel weights and utilizing logistic regression to swiftly adapt to task-specific adaptive loss functions. By conducting experiments on multiple open-source datasets for few-shot classification, cross-domain few-shot classification, and few-shot regression, the results indicate that the proposed ML-LRCA method achieves significant performance improvement.

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History
  • Received:August 15,2024
  • Revised:September 30,2024
  • Adopted:October 08,2024
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