# Smart Active Sampling to enhance Quality Assurance Efficiency

arXiv: https://arxiv.org/abs/2209.11464 · HTML: https://mlsanity.com/paper/2209.11464/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [A Data-Centric Approach for Training Deep Neural Networks with Less Data](https://mlsanity.com/paper/2110.03613/) (2021)
- [Class Imbalance Problem in Data Mining Review](https://mlsanity.com/paper/1305.1707/) (2013)
- [Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks](https://mlsanity.com/paper/2009.10835/) (2020)

## Built on by (most cited first, 1 of 1)

- [Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications](https://mlsanity.com/paper/2304.11101/) (2023)

## 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.