Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning
Machine Learning
2019-11-04 v1 Computer Vision and Pattern Recognition
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.
Cite
@article{arxiv.1911.00314,
title = {Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning},
author = {Ignasi Mas and Josep Ramon Morros and Veronica Vilaplana},
journal= {arXiv preprint arXiv:1911.00314},
year = {2019}
}