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EW‐CACTUs‐MAML: A Robust Metalearning System for Rapid Classification on a Large Number of Tasks

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Listed:
  • Wen-Feng Wang
  • Jingjing Zhang
  • Peng An

Abstract

This study aims to develop a robust metalearning system for rapid classification on a large number of tasks. The model‐agnostic metalearning (MAML) with the CACTUs method (clustering to automatically construct tasks for unsupervised metalearning) is improved as EW‐CACTUs‐MAML after integrated with the entropy weight (EW) method. Few‐shot mechanisms are introduced in the deep network for efficient learning of a large number of tasks. The process of implementation is theoretically interpreted as “gene intelligence.” Validation of EW‐CACTUs‐MAML on a typical dataset (Omniglot) indicates an accuracy of 97.42%, performing better than CACTUs‐MAML (validation accuracy = 97.22%). At the end of this paper, the availability of our thoughts to improve another metalearning system (EW‐CACTUs‐ProtoNets) is also preliminarily discussed based on a cross‐validation on another typical dataset (Miniimagenet).

Suggested Citation

  • Wen-Feng Wang & Jingjing Zhang & Peng An, 2022. "EW‐CACTUs‐MAML: A Robust Metalearning System for Rapid Classification on a Large Number of Tasks," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:7330823
    DOI: 10.1155/2022/7330823
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    References listed on IDEAS

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    1. Yan Zhang & Min Fang & Nian Wang, 2019. "Channel-spatial attention network for fewshot classification," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    2. Michael Brusco & J. Cradit, 2001. "A variable-selection heuristic for K-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 249-270, June.
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