A multi-features adaptive aggregation meta-learning method with an information enhancer is proposed for few-shot classification, which can effectively improve the generalization ability. An information enhancer is built to capture more valuable information in the process of feature extraction.
Oct 7, 2021 · This paper proposes a multi-features adaptive aggregation meta-learning method with an information enhancer for few-shot classification tasks, ...
Highlights•A multi-features adaptive aggregation meta-learning method is proposed.•An information enhancer is built to capture more valuable information.
Dec 1, 2021 · The information enhancer and MFAAC are connected by a hybrid loss, providing an excellent feature representation. During the meta-test stage, ...
The whole MFAML framework is solved by the optimization strategy of model-agnostic meta-learner (MAML) and can effectively improve generalization performance.
A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer · Hailiang Ye, Yi Wang, F. Cao · Published in Neural ...
A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer ... learning based on deep reinforcement learning.
Title: A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer. · Authors: Hailiang Ye, Yi Wang, Feilong Cao ...
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A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer. H Ye, Y Wang, F Cao. Neural Networks 144, 755-765, 2021.
A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer. Ye, Hailiang;Wang, Yi;Cao ...