Meta Networks

• Published in: Proceedings of machine learning research Vol. 70; pp. 2554 - 2563
• Main Authors: Munkhdalai, Tsendsuren, Yu, Hong
• Format: Journal Article
• Language: English
• Published: United States 01.08.2017
• ISSN: 2640-3498; 2640-3498

Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning.