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Meta Networks
Tsendsuren Munkhdalai · Hong Yu

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #94

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.

Author Information

Tsendsuren Munkhdalai (University of Massachusetts)
Hong Yu (University of Massachusetts)

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