# LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation

arXiv: https://arxiv.org/abs/2202.02661 · HTML: https://mlsanity.com/paper/2202.02661/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://mlsanity.com/paper/1809.08495/) (2018)
- [Active Learning for Deep Detection Neural Networks](https://mlsanity.com/paper/1911.09168/) (2019)
- [ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2107.11769/) (2021)
- [Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need](https://mlsanity.com/paper/1901.11409/) (2019)
- [Effective Evaluation of Deep Active Learning on Image Classification Tasks](https://mlsanity.com/paper/2106.15324/) (2021)
- [Importance of Self-Consistency in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2008.01860/) (2020)
- [Sparse MoEs meet Efficient Ensembles](https://mlsanity.com/paper/2110.03360/) (2021)
- [On Invariance and Selectivity in Representation Learning](https://mlsanity.com/paper/1503.05938/) (2015)
- [Bayesian active learning for production, a systematic study and a reusable library](https://mlsanity.com/paper/2006.09916/) (2020)
- [Deep Active Learning with Augmentation-based Consistency Estimation](https://mlsanity.com/paper/2011.02666/) (2020)

## Built on by (most cited first, 2 of 2)

- [Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles](https://mlsanity.com/paper/2302.08292/) (2023)
- [Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset](https://mlsanity.com/paper/2302.10679/) (2023)

## Abstract (arXiv)

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.