# LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

arXiv: https://arxiv.org/abs/2405.06875 · HTML: https://mlsanity.com/paper/2405.06875/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 27 of 27)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [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)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [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)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [A Unified Model for Multi-class Anomaly Detection](https://mlsanity.com/paper/2206.03687/) (2022)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Detecting the Unexpected via Image Resynthesis](https://mlsanity.com/paper/1904.07595/) (2019)
- [Pixel-wise Anomaly Detection in Complex Driving Scenes](https://mlsanity.com/paper/2103.05445/) (2021)
- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
- [Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation](https://mlsanity.com/paper/2003.08440/) (2020)
- [DSR -- A dual subspace re-projection network for surface anomaly detection](https://mlsanity.com/paper/2208.01521/) (2022)
- [A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video](https://mlsanity.com/paper/2008.12328/) (2020)
- [PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection](https://mlsanity.com/paper/2310.07716/) (2023)
- [G2D: Generate to Detect Anomaly](https://mlsanity.com/paper/2006.11629/) (2020)
- [Image Shape Manipulation from a Single Augmented Training Sample](https://mlsanity.com/paper/2007.01289/) (2020)
- [Set Features for Fine-grained Anomaly Detection](https://mlsanity.com/paper/2302.12245/) (2023)
- [SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification](https://mlsanity.com/paper/2305.00398/) (2023)

## Built on by (most cited first, 7 of 7)

- [Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection](https://mlsanity.com/paper/2502.11712/) (2025)
- [AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization](https://mlsanity.com/paper/2408.09533/) (2024)
- [AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection](https://mlsanity.com/paper/2504.04340/) (2025)
- [TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank](https://mlsanity.com/paper/2508.21795/) (2025)
- [When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection](https://mlsanity.com/paper/2605.26171/) (2026)
- [LogiCo: A Unified Framework for Logical and Structural Anomaly Detection](https://mlsanity.com/paper/2606.28688/) (2026)
- [Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies](https://mlsanity.com/paper/2610.18295/) (2026)

## Abstract (arXiv)

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraints. To fill the gap and boost anomaly localization performance, we propose an edge manipulation based anomaly synthesis framework, named LogicAL, that produces photo-realistic both logical and structural anomalies. We introduce a logical anomaly generation strategy that is adept at breaking logical constraints and a structural anomaly generation strategy that complements to the structural defects synthesis. We further improve the anomaly localization performance by introducing edge reconstruction into the network structure. Extensive experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets verify the advantage of proposed LogicAL on both logical and structural anomaly localization.