Aspect-Based Few-Shot Learning

TL;DR

This paper introduces aspect-based few-shot learning, allowing models to adapt to different contexts and perspectives without relying on predefined class labels, demonstrated on geometric datasets.

Contribution

It proposes a novel architecture and training method that develop and utilize aspects for flexible few-shot learning beyond fixed class sets.

Findings

Effective aspect formation on geometric datasets

Comparable performance to traditional few-shot methods

Demonstrates flexibility in context-based learning

Abstract

We generalize the formulation of few-shot learning by introducing the concept of an aspect. In the traditional formulation of few-shot learning, there is an underlying assumption that a single "true" label defines the content of each data point. This label serves as a basis for the comparison between the query object and the objects in the support set. However, when a human expert is asked to execute the same task without a predefined set of labels, they typically consider the rest of the data points in the support set as context. This context specifies the level of abstraction and the aspect from which the comparison can be made. In this work, we introduce a novel architecture and training procedure that develops a context given the query and support set and implements aspect-based few-shot learning that is not limited to a predetermined set of classes. We demonstrate that our method is capable of forming and using an aspect for few-shot learning on the Geometric Shapes and Sprites dataset. The results validate the feasibility of our approach compared to traditional few-shot learning.