# Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control

arXiv: https://arxiv.org/abs/2408.11561 · HTML: https://mlsanity.com/paper/2408.11561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [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)
- [DSR -- A dual subspace re-projection network for surface anomaly detection](https://mlsanity.com/paper/2208.01521/) (2022)
- [Robust Anomaly Detection in Images using Adversarial Autoencoders](https://mlsanity.com/paper/1901.06355/) (2019)
- [Anomaly Detection by Robust Statistics](https://mlsanity.com/paper/1707.09752/) (2017)
- [End-to-end training of a two-stage neural network for defect detection](https://mlsanity.com/paper/2007.07676/) (2020)
- [Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection](https://mlsanity.com/paper/2406.00501/) (2024)
- [Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection](https://mlsanity.com/paper/2407.03961/) (2024)
- [RAD: On-line Anomaly Detection for Highly Unreliable Data](https://mlsanity.com/paper/1911.04383/) (2019)
- [Robust Anomaly Detection for Time-series Data](https://mlsanity.com/paper/2202.02721/) (2022)

## 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)
- [Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection](https://mlsanity.com/paper/2503.01569/) (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 introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness using two benchmark datasets, Kolektor SDD2 (KSDD2) and MVTec AD, covering a wide range of industrial products and defect types. Our experimental results demonstrate that the IRP consistently outperforms traditional anomaly detection models, particularly in environments with high noise levels. This study highlights the IRP's potential to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.