# Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection

arXiv: https://arxiv.org/abs/2308.02983 · HTML: https://mlsanity.com/paper/2308.02983/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 66 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [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)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [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)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [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)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [A Unified Model for Multi-class Anomaly Detection](https://mlsanity.com/paper/2206.03687/) (2022)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)

## Built on by (most cited first, 30 of 32)

- [Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts](https://mlsanity.com/paper/2403.06495/) (2024)
- [Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection](https://mlsanity.com/paper/2310.19070/) (2023)
- [AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection](https://mlsanity.com/paper/2505.09926/) (2025)
- [Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection](https://mlsanity.com/paper/2403.13349/) (2024)
- [SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning](https://mlsanity.com/paper/2410.14987/) (2024)
- [Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2310.12790/) (2023)
- [ResAD: A Simple Framework for Class Generalizable Anomaly Detection](https://mlsanity.com/paper/2410.20047/) (2024)
- [Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2410.10289/) (2024)
- [Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection](https://mlsanity.com/paper/2403.11561/) (2024)
- [Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt](https://mlsanity.com/paper/2505.09264/) (2025)
- [CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation](https://mlsanity.com/paper/2406.18941/) (2024)
- [MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning](https://mlsanity.com/paper/2505.09265/) (2025)
- [VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer](https://mlsanity.com/paper/2603.07952/) (2026)
- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [A SAM-guided Two-stream Lightweight Model for Anomaly Detection](https://mlsanity.com/paper/2402.19145/) (2024)
- [Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning](https://mlsanity.com/paper/2411.17217/) (2024)
- [Towards Accurate Unified Anomaly Segmentation](https://mlsanity.com/paper/2501.12295/) (2025)
- [SALAD -- Semantics-Aware Logical Anomaly Detection](https://mlsanity.com/paper/2509.02101/) (2025)
- [One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection](https://mlsanity.com/paper/2601.05552/) (2026)
- [Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection](https://mlsanity.com/paper/2405.02068/) (2024)
- [KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices](https://mlsanity.com/paper/2505.24334/) (2025)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection](https://mlsanity.com/paper/2311.18332/) (2023)
- [URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2603.22840/) (2026)
- [MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2604.10971/) (2026)
- [Bi-Grid Reconstruction for Image Anomaly Detection](https://mlsanity.com/paper/2504.00609/) (2025)
- [Domain Adaptation of Attention Heads for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2505.22259/) (2025)
- [Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments](https://mlsanity.com/paper/2506.16050/) (2025)
- [A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection](https://mlsanity.com/paper/2603.22861/) (2026)
- [InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation](https://mlsanity.com/paper/2604.04632/) (2026)

## Abstract (arXiv)

Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD methods didn't sufficiently combine the two complementary aspects to design AD models. To this end, we find that Transformer can ideally satisfy the two aspects as its great power in the unified modeling of patch-wise representations and patch-to-patch correlations. In this paper, we propose a novel AD framework: FOcus-the-Discrepancy (FOD), which can simultaneously spot the patch-wise, intra- and inter-discrepancies of anomalies. The major characteristic of our method is that we renovate the self-attention maps in transformers to Intra-Inter-Correlation (I2Correlation). The I2Correlation contains a two-branch structure to first explicitly establish intra- and inter-image correlations, and then fuses the features of two-branch to spotlight the abnormal patterns. To learn the intra- and inter-correlations adaptively, we propose the RBF-kernel-based target-correlations as learning targets for self-supervised learning. Besides, we introduce an entropy constraint strategy to solve the mode collapse issue in optimization and further amplify the normal-abnormal distinguishability. Extensive experiments on three unsupervised real-world AD benchmarks show the superior performance of our approach. Code will be available at https://github.com/xcyao00/FOD.