Title: Meta Networks,
Abstract: 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.