EW‐CACTUs‐MAML: A Robust Metalearning System for Rapid Classification on a Large Number of Tasks
Wenfeng Wang, Jingjing Zhang, Peng An · Complexity · 2022
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).