Learning a Universal Template for Few-shot Dataset Generalization

• URL: http://arxiv.org/abs/2105.07029v1
• Date: Fri, 14 May 2021 18:46:06 GMT
• Title: Learning a Universal Template for Few-shot Dataset Generalization
• Authors: Eleni Triantafillou, Hugo Larochelle, Richard Zemel and Vincent Dumoulin
• Abstract summary: Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem. We propose to utilize a diverse training set to construct a universal template that can define a wide array of dataset-specialized models. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.
• Score: 25.132729497191047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples. To this end, we propose to utilize the diverse training set to construct a universal template: a partial model that can define a wide array of dataset-specialized models, by plugging in appropriate components. For each new few-shot classification problem, our approach therefore only requires inferring a small number of parameters to insert into the universal template. We design a separate network that produces an initialization of those parameters for each given task, and we then fine-tune its proposed initialization via a few steps of gradient descent. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.

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