Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning

TL;DR

This paper introduces a novel static pool-based meta-active learning approach that selects samples considering the entire subset, aiming to improve label efficiency in costly annotation scenarios.

Contribution

It extends existing meta-active learning methods by enabling selection based on the entire chosen subset, not just individual samples.

Findings

Enhanced sample selection considering the whole subset

Improved label efficiency in active learning scenarios

Potential for better generalization in annotation tasks

Abstract

Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta- Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.