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Meta Networks
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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.
Forward citations
Cited by 2 Pith papers
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Efficient Automatic Meta Optimization Search for Few-Shot Learning
A NAS controller and Reptile meta-learning are jointly optimized to automatically search few-shot learner architectures, reaching 74.2% on Mini-ImageNet 5-shot 5-way transductive classification in 1 to 2 GPU days.
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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
ViT-ProtoNet, a Prototypical Network with a ViT-Small encoder, is reported to reach 95-97% 5-shot accuracy on three benchmarks and 81.88% on FC100, but the evaluation lacks critical baselines.
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