mlsanityData to 14 Oct 2026

Smart Active Sampling to enhance Quality Assurance Efficiency

arXiv 2209.11464 ↗First posted 23 Sep 2022cs.LGcited by 1

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Abstract (arXiv)

We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line. Based on the principles of active learning a machine learning model decides which samples are sent to quality inspection. On the one hand, this minimizes the production of scrap parts due to earlier detection of quality violations. On the other hand, quality inspection costs are reduced for smooth operation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 23 Sep 2022.

Builds on

3 of 14 references.

References · 3

  1. 2021A Data-Centric Approach for Training Deep Neural Networks with Less Datacited by 28
  2. 2013Class Imbalance Problem in Data Mining Reviewcited by 16
  3. 2020Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networkscited by 5

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applicationscited by 4