# Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection

arXiv: https://arxiv.org/abs/2503.01569 · HTML: https://mlsanity.com/paper/2503.01569/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
- [Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control](https://mlsanity.com/paper/2408.11561/) (2024)

## Built on by (most cited first, 5 of 5)

- [Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning](https://mlsanity.com/paper/2508.02293/) (2025)
- [A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection](https://mlsanity.com/paper/2508.17827/) (2025)
- [ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis](https://mlsanity.com/paper/2507.15335/) (2025)
- [RoadFusion: Latent Diffusion Model for Pavement Defect Detection](https://mlsanity.com/paper/2507.15346/) (2025)
- [Robust Anomaly Detection in Industrial Environments via Meta-Learning](https://mlsanity.com/paper/2508.17789/) (2025)

## Abstract (arXiv)

This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta learning approaches to identify and reject noisy training data to improve the learning process. In our model, we employ Model Agnostic Meta Learning (MAML) and an iterative refinement process through an Inter-Quartile Range rejection scheme to enhance their adaptability and robustness. This approach significantly improves the models capability to distinguish between normal and defective conditions. Our results of experiments conducted on well known MVTec and KSDD2 datasets demonstrate that the proposed method not only excels in environments with substantial noise but can also contribute in case of a clear training set, isolating those samples that are relatively out of distribution, thus offering significant improvements over traditional models.