BaseTransformers: Attention over base data-points for One Shot Learning

TL;DR

BaseTransformers enhances one-shot learning by selectively attending to relevant base data points, improving support instance representations and achieving state-of-the-art results across benchmarks.

Contribution

It introduces a novel attention mechanism over base data points to refine support representations during meta-test, addressing distribution mismatch issues.

Findings

Achieves state-of-the-art results on three benchmark datasets.

Effective across multiple backbone architectures.

Improves support representation quality during meta-test.

Abstract

Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled examples to train an encoder that is used for obtaining representations of support instances for novel classes. Since the test instances are from a distribution different to the base distribution, their feature representations are of poor quality, degrading performance. In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time. To this end, we propose BaseTransformers, that attends to the most relevant regions of the base dataset feature space and improves support instance representations. Experiments on three benchmark data sets show that our method works well for several backbones and achieves state-of-the-art results in the inductive one shot setting. Code is available at github.com/mayug/BaseTransformers