Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset

• DOI: 10.48550/arxiv.2302.10679
• Publication Date: 2023-01-01
• Authors: Duong, Anh; Almin, Alexandre; Lemarié, Léo; Kiran, B Ravi
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Machine Learning (cs.LG)

Submitted to VISAPP Springer book extension. arXiv admin note: substantial text overlap with arXiv:2202.02661<br/>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 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.<br/>