One-shot Learning with Absolute Generalization

• URL: http://arxiv.org/abs/2105.13559v1
• Date: Fri, 28 May 2021 02:52:52 GMT
• Title: One-shot Learning with Absolute Generalization
• Authors: Hao Su
• Abstract summary: We propose a set of definitions to explain what kind of datasets can support one-shot learning. Based on these definitions, we proposed a method to build an absolutely generalizable classifier. Experiments demonstrate that the proposed method is superior to baseline on one-shot learning datasets and artificial datasets.
• Score: 23.77607345586489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One-shot learning is proposed to make a pretrained classifier workable on a new dataset based on one labeled samples from each pattern. However, few of researchers consider whether the dataset itself supports one-shot learning. In this paper, we propose a set of definitions to explain what kind of datasets can support one-shot learning and propose the concept "absolute generalization". Based on these definitions, we proposed a method to build an absolutely generalizable classifier. The proposed method concatenates two samples as a new single sample, and converts a classification problem to an identity identification problem or a similarity metric problem. Experiments demonstrate that the proposed method is superior to baseline on one-shot learning datasets and artificial datasets.

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