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

Heuristics
DOI: 10.48550/arxiv.2302.10679 Publication Date: 2023-01-01
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 effect application data augmentation (DA) within AL based distillation. perform these experiments on Semantic-KITTI dataset. extend our over existing work only 1/4th same Addition DA and BALD have a negative impact labeling efficiency thus capacity distill demonstrate key issues designing functional framework finally conclude review challenges real world learning.
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