# PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation

arXiv: https://arxiv.org/abs/2203.14457 · HTML: https://mlsanity.com/paper/2203.14457/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2104.10036/) (2021)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [Detecting the Unexpected via Image Resynthesis](https://mlsanity.com/paper/1904.07595/) (2019)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Anomalib: A Deep Learning Library for Anomaly Detection](https://mlsanity.com/paper/2202.08341/) (2022)
- [Towards Deeper Understanding of Variational Autoencoding Models](https://mlsanity.com/paper/1702.08658/) (2017)
- [Deep Metric Learning for Open World Semantic Segmentation](https://mlsanity.com/paper/2108.04562/) (2021)
- [Dense outlier detection and open-set recognition based on training with noisy negative images](https://mlsanity.com/paper/2101.09193/) (2021)
- [Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images](https://mlsanity.com/paper/1806.06530/) (2018)
- [Additive Tensor Decomposition Considering Structural Data Information](https://mlsanity.com/paper/2007.13860/) (2020)
- [Compressed Smooth Sparse Decomposition](https://mlsanity.com/paper/2201.07404/) (2022)
- [Screen Content Image Segmentation Using Sparse-Smooth Decomposition](https://mlsanity.com/paper/1511.06911/) (2015)

## Built on by (most cited first, 6 of 6)

- [RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection](https://mlsanity.com/paper/2302.12464/) (2023)
- [Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Products](https://mlsanity.com/paper/2408.16201/) (2024)
- [Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization](https://mlsanity.com/paper/2410.18490/) (2024)
- [RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2508.02903/) (2025)
- [Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches](https://mlsanity.com/paper/2309.11120/) (2023)
- [Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models](https://mlsanity.com/paper/2508.04818/) (2025)

## Abstract (arXiv)

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective region localization but lack accuracy in defective region shape contour extraction; reconstruction-based methods detected defective region match well with the ground truth defective region shape contour but are noisy. To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation. In the training stage, we learn the common background as a deep image prior by a patch autoencoder (PAE) network. In the inference stage, we formulate anomaly detection as an image decomposition problem with the deep image prior and domain-specific regularizations. By adopting the proposed approach, the defective regions in the image can be accurately extracted in an unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in simulation studies and an industrial dataset in the case study.