Meta Networks
This addresses the problem of few-shot learning for neural networks, enabling better adaptation to new tasks with limited data, which is incremental as it builds on existing meta-learning methods.
The paper tackles the challenge of rapid generalization on new concepts with small training data while preserving performance on previously learned tasks, achieving near human-level performance and outperforming baseline approaches by up to 6% accuracy on Omniglot and Mini-ImageNet benchmarks.
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.