Domain-Based Semi-Supervised Learning: Exploiting Label Invariance in Unlabeled Data from Distributed Cameras
Abstract
In several practical supervised learning problems where we have a large amount of data from distributed cameras or sensors, we can use domain knowledge to identify subsets of unlabeled examples with the same (unknown) label. Under this assumption, we propose a straightforward way to exploit label invariance in unlabeled data within a domain-aware semi-supervised learning framework (DSSL). Our approach exploits such invariance to generate higher quality pseudolabels to be used in a consistency loss term.We report experiments and ablation studies on three practical cases on data from real-world fleets of connected vehicles that naturally exhibit the required assumption: an image classification problem, a semantic segmentation task, and a time series classification one. We show that our approach is extremely effective, especially when few labeled samples are available, and can be easily adapted to tasks of different nature.
Cite
Text
Taccari. "Domain-Based Semi-Supervised Learning: Exploiting Label Invariance in Unlabeled Data from Distributed Cameras." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00037Markdown
[Taccari. "Domain-Based Semi-Supervised Learning: Exploiting Label Invariance in Unlabeled Data from Distributed Cameras." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/taccari2021iccvw-domainbased/) doi:10.1109/ICCVW54120.2021.00037BibTeX
@inproceedings{taccari2021iccvw-domainbased,
title = {{Domain-Based Semi-Supervised Learning: Exploiting Label Invariance in Unlabeled Data from Distributed Cameras}},
author = {Taccari, Leonardo},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2021},
pages = {290-297},
doi = {10.1109/ICCVW54120.2021.00037},
url = {https://mlanthology.org/iccvw/2021/taccari2021iccvw-domainbased/}
}