Active Decision Boundary Annotation with Deep Generative Models
Abstract
This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d vector. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three datasets that our method can be plugged-in to other active learning schemes, that human oracles can effectively annotate point on the decision boundary, and that decision boundary annotations improve over single sample instance annotations.
Cite
Text
Huijser and van Gemert. "Active Decision Boundary Annotation with Deep Generative Models." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.565Markdown
[Huijser and van Gemert. "Active Decision Boundary Annotation with Deep Generative Models." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/huijser2017iccv-active/) doi:10.1109/ICCV.2017.565BibTeX
@inproceedings{huijser2017iccv-active,
title = {{Active Decision Boundary Annotation with Deep Generative Models}},
author = {Huijser, Miriam and van Gemert, Jan C.},
booktitle = {International Conference on Computer Vision},
year = {2017},
doi = {10.1109/ICCV.2017.565},
url = {https://mlanthology.org/iccv/2017/huijser2017iccv-active/}
}