A Relational Model for One-Shot Classification

• URL: http://arxiv.org/abs/2111.04313v1
• Date: Mon, 8 Nov 2021 07:53:12 GMT
• Title: A Relational Model for One-Shot Classification
• Authors: Arturs Polis and Alexander Ilin
• Abstract summary: We show that a deep learning model with built-in inductive bias can bring benefits to sample-efficient learning, without relying on extensive data augmentation. The proposed one-shot classification model performs relational matching of a pair of inputs in the form of local and pairwise attention.
• Score: 80.77724423309184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We show that a deep learning model with built-in relational inductive bias can bring benefits to sample-efficient learning, without relying on extensive data augmentation. The proposed one-shot classification model performs relational matching of a pair of inputs in the form of local and pairwise attention. Our approach solves perfectly the one-shot image classification Omniglot challenge. Our model exceeds human level accuracy, as well as the previous state of the art, with no data augmentation.

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