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A Relational Model for One-Shot Classification

2021-11-08 07:53:12
Arturs Polis, Alexander Ilin

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.

Abstract (translated)

URL

https://arxiv.org/abs/2111.04313

PDF

https://arxiv.org/pdf/2111.04313.pdf


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