# ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems

arXiv: https://arxiv.org/abs/2404.00572 · HTML: https://mlsanity.com/paper/2404.00572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 73 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://mlsanity.com/paper/2007.08176/) (2020)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Cost-Effective Active Learning for Deep Image Classification](https://mlsanity.com/paper/1701.03551/) (2017)
- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [Generative Adversarial Active Learning](https://mlsanity.com/paper/1702.07956/) (2017)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [Bayesian Generative Active Deep Learning](https://mlsanity.com/paper/1904.11643/) (2019)
- [Debiased Contrastive Learning of Unsupervised Sentence Representations](https://mlsanity.com/paper/2205.00656/) (2022)
- [A new similarity measure for covariate shift with applications to nonparametric regression](https://mlsanity.com/paper/2202.02837/) (2022)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [Active Self-Paced Learning for Cost-Effective and Progressive Face Identification](https://mlsanity.com/paper/1701.03555/) (2017)
- [Convergence of Uncertainty Sampling for Active Learning](https://mlsanity.com/paper/2110.15784/) (2021)
- [Cost-Accuracy Aware Adaptive Labeling for Active Learning](https://mlsanity.com/paper/2105.11418/) (2021)
- [WOOD: Wasserstein-based Out-of-Distribution Detection](https://mlsanity.com/paper/2112.06384/) (2021)
- [Failure-averse Active Learning for Physics-constrained Systems](https://mlsanity.com/paper/2110.14443/) (2021)
- [Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System](https://mlsanity.com/paper/2302.12004/) (2023)
- [Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images](https://mlsanity.com/paper/2306.06908/) (2023)
- [Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets](https://mlsanity.com/paper/2206.06355/) (2022)

## Abstract (arXiv)

Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data. However, data collection needs extensive time costs and investments in the manufacturing system, and data scarcity commonly exists. Therefore, data-sharing is widely enabled among multiple machines with similar functionality to augment the dataset for building ML methods. However, distribution mismatch inevitably exists in their data due to different working conditions, while the ML methods are assumed to be built and tested on the dataset following the same distribution. Thus, an Active Data-sharing (ADs) framework is proposed to ensure the quality of the shared data among multiple machines. It is designed to simultaneously select the most informative data points benefiting the downstream tasks and mitigate the distribution mismatch among all selected data points. The proposed method is validated on anomaly detection on in-situ monitoring data from three additive manufacturing processes.