# Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

arXiv: https://arxiv.org/abs/2101.00509 · HTML: https://mlsanity.com/paper/2101.00509/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning](https://mlsanity.com/paper/2012.03301/) (2020)

## Built on by (most cited first, 19 of 19)

- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [A Theory for Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2208.06931/) (2022)
- [Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning](https://mlsanity.com/paper/2403.15463/) (2024)
- [Continual Learning Approaches for Anomaly Detection](https://mlsanity.com/paper/2212.11192/) (2022)
- [Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries](https://mlsanity.com/paper/2107.03336/) (2021)
- [Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach](https://mlsanity.com/paper/2407.09039/) (2024)
- [Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data](https://mlsanity.com/paper/2106.04920/) (2021)
- [Contrastive Continual Learning for Model Adaptability in Internet of Things](https://mlsanity.com/paper/2602.04881/) (2026)
- [Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning](https://mlsanity.com/paper/2207.12831/) (2022)
- [Boosting-inspired online learning with transfer for railway maintenance](https://mlsanity.com/paper/2504.08554/) (2025)
- [Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems](https://mlsanity.com/paper/2105.14730/) (2021)
- [Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing](https://mlsanity.com/paper/2312.02491/) (2023)
- [Continual learning for surface defect segmentation by subnetwork creation and selection](https://mlsanity.com/paper/2312.05100/) (2023)
- [Incremental Learning with Repetition via Pseudo-Feature Projection](https://mlsanity.com/paper/2502.19922/) (2025)
- [Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection](https://mlsanity.com/paper/2204.01620/) (2022)
- [Slowing Down Forgetting in Continual Learning](https://mlsanity.com/paper/2411.06916/) (2024)
- [ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting](https://mlsanity.com/paper/2411.07413/) (2024)
- [Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding](https://mlsanity.com/paper/2508.16832/) (2025)

## Abstract (arXiv)

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset.