Abstract
Active Learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the full Semantic-KITTI dataset. We extend our study over our existing work [14] only on 1/4th of the same dataset. Addition of DA and BALD have a negative impact over the labeling efficiency and thus the capacity to distill datasets. We demonstrate key issues in designing a functional AL framework and finally conclude with a review of challenges in real world active learning.
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Acknowledgements
This work was granted access to HPC resources of [TGCC/ CINES/IDRIS] under the allocation 2021- [AD011012836] made by GENCI (Grand Equipment National de Calcul Intensif). It is also part of the Deep Learning Segmentation (DLS) project financed by ADEME.
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Duong, N.P.A., Almin, A., Lemarié, L., Kiran, B.R. (2023). Active Learning with Data Augmentation Under Small vs Large Dataset Regimes for Semantic-KITTI Dataset. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2022. Communications in Computer and Information Science, vol 1815. Springer, Cham. https://doi.org/10.1007/978-3-031-45725-8_13
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