Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach
Abstract
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for highly contaminated datasets is, however, still challenging due to its underlying computational complexity. Recent attention has been focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems.
Cite
Text
Truong et al. "Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.01021Markdown
[Truong et al. "Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/truong2021cvpr-unsupervised/) doi:10.1109/CVPR46437.2021.01021BibTeX
@inproceedings{truong2021cvpr-unsupervised,
title = {{Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach}},
author = {Truong, Giang and Le, Huu and Suter, David and Zhang, Erchuan and Gilani, Syed Zulqarnain},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2021},
pages = {10348-10357},
doi = {10.1109/CVPR46437.2021.01021},
url = {https://mlanthology.org/cvpr/2021/truong2021cvpr-unsupervised/}
}