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

arXiv 2302.10679 ↗First posted 21 Feb 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 21 Feb 2023.

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24 of 37 references.

References · 24

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12 more
  1. 2021ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentationcited by 52
  2. 2019Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Needcited by 43
  3. 2021Effective Evaluation of Deep Active Learning on Image Classification Taskscited by 37
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  11. 2020Deep Active Learning with Augmentation-based Consistency Estimationcited by 8
  12. 2022LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentationcited by 2

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