# Anomaly localization by modeling perceptual features

arXiv: https://arxiv.org/abs/2008.05369 · HTML: https://mlsanity.com/paper/2008.05369/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2405.14325/) (2024)
- [Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.13118/) (2021)
- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [Anomaly Detection with Conditioned Denoising Diffusion Models](https://mlsanity.com/paper/2305.15956/) (2023)
- [DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection](https://mlsanity.com/paper/2303.08730/) (2023)
- [PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection](https://mlsanity.com/paper/2310.07716/) (2023)
- [Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt](https://mlsanity.com/paper/2401.01010/) (2024)
- [Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval](https://mlsanity.com/paper/2308.06748/) (2023)
- [Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation](https://mlsanity.com/paper/2209.12441/) (2022)
- [Anomaly Detection via Self-organizing Map](https://mlsanity.com/paper/2107.09903/) (2021)
- [Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection](https://mlsanity.com/paper/2412.17458/) (2024)
- [Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2412.17619/) (2024)
- [What makes a good data augmentation for few-shot unsupervised image anomaly detection?](https://mlsanity.com/paper/2304.03294/) (2023)
- [Re-parameterizing VAEs for stability](https://mlsanity.com/paper/2106.13739/) (2021)
- [Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers](https://mlsanity.com/paper/2306.03492/) (2023)
- [RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations](https://mlsanity.com/paper/2410.00713/) (2024)
- [ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement](https://mlsanity.com/paper/2407.17705/) (2024)
- [Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2402.00448/) (2024)
- [Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection](https://mlsanity.com/paper/2603.21562/) (2026)

## Abstract (arXiv)

Although unsupervised generative modeling of an image dataset using a Variational AutoEncoder (VAE) has been used to detect anomalous images, or anomalous regions in images, recent works have shown that this method often identifies images or regions that do not concur with human perception, even questioning the usability of generative models for robust anomaly detection. Here, we argue that those issues can emerge from having a simplistic model of the anomaly distribution and we propose a new VAE-based model expressing a more complex anomaly model that is also closer to human perception. This Feature-Augmented VAE is trained by not only reconstructing the input image in pixel space, but also in several different feature spaces, which are computed by a convolutional neural network trained beforehand on a large image dataset. It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.