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

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  • Wen-Feng Wang
  • Jingjing Zhang
  • Peng An
  • Zhijie Wang

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 & Zhijie Wang, 2022. "EW-CACTUs-MAML: A Robust Metalearning System for Rapid Classification on a Large Number of Tasks," Complexity, Hindawi, vol. 2022, pages 1-8, February.
    • Handle: RePEc:hin:complx:7330823
    DOI: 10.1155/2022/7330823
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