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Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry

arXiv 2110.09023 ↗First posted 18 Oct 2021cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with high-volume visual inspections due to the car models' increasing complexity. Even though the application of machine learning to many visual inspection tasks has demonstrated great success, its need for large labeled data sets remains a central barrier to using such systems in practice. In this paper, we propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings without compromising performance. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 18 Oct 2021.

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References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  5. 2017A Survey on Multi-Task Learningcited by 838
  6. 2017Deep Bayesian Active Learning with Image Datacited by 812
  7. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  8. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  9. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  10. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  11. 2019Learning Loss for Active Learningcited by 335
  12. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
12 more
  1. 2016Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessmentcited by 224
  2. 2017Cost-Effective Active Learning for Deep Image Classificationcited by 165
  3. 2018Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Networkcited by 74
  4. 2018Adversarial Sampling for Active Learningcited by 51
  5. 2017Human-in-the-loop Artificial Intelligencecited by 40
  6. 2020DEAL: Deep Evidential Active Learning for Image Classificationcited by 22
  7. 2018Deep Learning in the Wildcited by 12
  8. 2021labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Cloudscited by 8
  9. 2020An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysiscited by 7
  10. 2020A network analysis on cloud gaming: Stadia, GeForce Now and PSNowcited by 5
  11. 2020Towards Deep Learning Methods for Quality Assessment of Computer-Generated Imagerycited by 2
  12. 2020Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industrycited by 2

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