Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning
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
Text
Mas et al. "Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00171Markdown
[Mas et al. "Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/mas2019iccvw-picking/) doi:10.1109/ICCVW.2019.00171BibTeX
@inproceedings{mas2019iccvw-picking,
title = {{Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning}},
author = {Mas, Ignasi and Morros, Ramon and Vilaplana, Verónica},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {1354-1362},
doi = {10.1109/ICCVW.2019.00171},
url = {https://mlanthology.org/iccvw/2019/mas2019iccvw-picking/}
}