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arXiv · 1806.01010

Meta-Learner with Linear Nulling

Sung Whan Yoon·Jun Seo·Jaekyun Moon
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Abstract

We propose a meta-learning algorithm utilizing a linear transformer that carries out null-space projection of neural network outputs. The main idea is to construct an alternative classification space such that the error signals during few-shot learning are quickly zero-forced on that space so that reliable classification on low data is possible. The final decision on a query is obtained utilizing a null-space-projected distance measure between the network output and reference vectors, both of which have been trained in the initial learning phase. Among the known methods with a given model size, our meta-learner achieves the best or near-best image classification accuracies with Omniglot and miniImageNet datasets.

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Sung Whan Yoon, Jun Seo, Jaekyun Moon. 2018-06-04. Meta-Learner with Linear Nulling. https://arxiv.org/abs/1806.01010

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