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Authors: Anh Ngoc Phuong  Duong ; Alexandre Almin ; Léo Lemarié and B. Ravi Kiran

Affiliation: Machine Learning, Navya, France

Keyword(s): Active Learning, Point Clouds, Semantic Segmentation, Data Augmentation, Label Efficiency, Dataset Distillation.

Abstract: Autonomous driving (AD) datasets have progressively grown in size in the past few years to enable better deep representation learning. Active learning (AL) has re-gained attention recently to address reduction of annotation costs and dataset size. AL has remained relatively unexplored for AD datasets, especially on point cloud data from LiDARs. This paper performs a principled evaluation of AL based dataset distillation on (1/4th) of the large Semantic-KITTI dataset. Further on, the gains in model performance due to data augmentation (DA) are demonstrated across different subsets of the AL loop. We also demonstrate how DA improves the selection of informative samples to annotate. We observe that data augmentation achieves full dataset accuracy using only 60% of samples from the selected dataset configuration. This provides faster training time and subsequent gains in annotation costs.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Duong, A. ; Almin, A. ; Lemarié, L. and Kiran, B. (2022). LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 159-167. DOI: 10.5220/0010860800003124

@conference{visapp22,
author={Anh Ngoc Phuong Duong and Alexandre Almin and Léo Lemarié and B. Ravi Kiran},
title={LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={159-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010860800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation
SN - 978-989-758-555-5
IS - 2184-4321
AU - Duong, A.
AU - Almin, A.
AU - Lemarié, L.
AU - Kiran, B.
PY - 2022
SP - 159
EP - 167
DO - 10.5220/0010860800003124
PB - SciTePress

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