A Relational Model for One-Shot Classification of Images and Pen Strokes

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. Our study shows that excellent results can be achieved with a model in which the relational inductive bias is applied to images, while building an efficient one-shot classifier on top of raw strokes is more challenging. 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 with almost perfect accuracy the one-shot image classification Omniglot challenge when combined with a Hungarian matching algorithm and attains competitive results on the same task on characters represented as rotation-augmented strokes.