Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

Gradient-based learning applied to document recognition

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Deep Learning for Anomaly Detection: A Review

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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

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Deep Learning for Anomaly Detection: A Review

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Anomaly Detection with Robust Deep Autoencoders

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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

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Learning Memory-Guided Normality for Anomaly Detection

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Deep Anomaly Detection with Outlier Exposure

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Adversarially Learned One-Class Classifier for Novelty Detection

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OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations

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Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation

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Revisiting multiple instance neural networks

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Deep Anomaly Detection Using Geometric TransformationsPreprint

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Deep Anomaly Detection with Deviation Networks

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Anomaly Detection using One-Class Neural NetworksPreprint

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Likelihood Ratios for Out-of-Distribution DetectionPreprint

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Learning Deep Features for One-Class Classification

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Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection

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Deep Semi-Supervised Anomaly Detection

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Toward Supervised Anomaly Detection

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Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

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Unmasking the Abnormal Events in Video

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PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

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Classification-Based Anomaly Detection for General DataPreprint

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Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model

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Learning Placeholders for Open-Set Recognition

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Attention Guided Anomaly Localization in Images

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Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

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Graph Embedded Pose Clustering for Anomaly Detection

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Counterfactual Zero-Shot and Open-Set Visual Recognition

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Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

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Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

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CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection

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Explainable Deep One-Class Classification

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A Public Fabric Database for Defect Detection Methods and Results

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MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space

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Pixel-wise Anomaly Detection in Complex Driving Scenes

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Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

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Learning and Evaluating Representations for Deep One-class ClassificationPreprint

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Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies

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Deep One-Class Classification via Interpolated Gaussian Descriptor

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Toward Deep Supervised Anomaly Detection

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Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

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Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

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MOOD: Multi-level Out-of-distribution Detection

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Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images

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Glancing at the Patch: Anomaly Localization with Global and Local Feature Comparison

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Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces

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Explainable Deep Few-shot Anomaly Detection with Deviation NetworksPreprint

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Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

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Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

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Weakly Supervised Learning for Industrial Optical Inspection

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What’s Wrong with That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution

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Deep Industrial Image Anomaly Detection: A Survey

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A Diffusion-Based Framework for Multi-Class Anomaly Detection

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AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

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AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

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A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

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Deep Weakly-supervised Anomaly Detection

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A survey of deep learning for industrial visual anomaly detection

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Prototypical Residual Networks for Anomaly Detection and Localization

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Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

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Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

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IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

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OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization

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Deep Graph Anomaly Detection: A Survey and New Perspectives

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Toward Generalist Anomaly Detection via In-Context Residual Learning with Few-Shot Sample Prompts

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Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

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Anomaly Detection under Distribution Shift

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RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

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PromptAD: Zero-shot Anomaly Detection using Text Prompts

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Graph Anomaly Detection in Time Series: A Survey

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Target Before Shooting: Accurate Anomaly Detection and Localization Under One Millisecond via Cascade Patch Retrieval

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UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection

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BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization

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SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

Rolih, B., Fučka, M., & Skočaj, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78192-6_4 · Cited by 33 · see citing works

Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation

Jin, Y., Peng, J., He, Q., Hu, T., Wu, J., Chen, H., Wang, H., Zhu, W., Chi, M., Liu, J., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02832 · Cited by 33 · see citing works

Self-Supervised Normalizing Flows for Image Anomaly Detection and Localization

Chiu, L.-L., & Lai, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00294 · Cited by 32 · see citing works

Hyperbolic Anomaly Detection

Li, H., Chen, Z., Xu, Y., & Hu, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01658 · Cited by 31 · see citing works

A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts

Shukla, V., Shukla, A., K, S. P. S., & Shukla, S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1554196 · Cited by 35 · see citing works

Deep Learning for Video Anomaly Detection: A Review

Wu, P., Pan, C., Yan, Y., Pang, G., Yan, Q., Wang, P., & zhang, Y. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3647892 · Cited by 31 · see citing works

UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection

Wei, S., Jiang, J., & Xu, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00934 · Cited by 30 · see citing works

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Su, Z., Rezapour, M., Sajjad, U., Gürcan, M. N., & Niazi, M. K. K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107607 · Cited by 27 · see citing works

SoftPatch+: Fully unsupervised anomaly classification and segmentation

Wang, C., Jiang, X., Gao, B.-B., Gan, Z., Liu, Y., Zheng, F., & Ma, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111295 · Cited by 27 · see citing works

TF²: Few-Shot Text-Free Training-Free Defect Image Generation for Industrial Anomaly Inspection

Yu, Q., Zhu, K., Cao, Y. H., Xia, F., & Kang, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3424435 · Cited by 26 · see citing works

SDDiff: Semi-supervised surface defect detection with Diffusion Probabilistic Model

Wang, X., Li, W., Liu, C., & Ouyang, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.115276 · Cited by 26 · see citing works

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min, Y., Wang, Z., Liu, Y., & Wang, Z. (2023) · Sensors · doi:10.3390/s23187894 · Cited by 20 · see citing works

FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries

Jiang, Y., Lu, X., Jin, Q., Sun, Q. L., Wu, H., & Zhuo, C. (2024) · doi:10.1145/3676536.3676750 · Cited by 22 · see citing works

Memory Clustering Autoencoder Method for Human Action Anomaly Detection on Surveillance Camera Video

Yan, M., Xiong, Y., & She, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3239219 · Cited by 20 · see citing works

ADPS: Asymmetric Distillation Postsegmentation for Image Anomaly Detection

Xing, P., Tang, H., Tang, J., & Li, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3390806 · Cited by 19 · see citing works

AADC-Net: A Multimodal Deep Learning Framework for Automatic Anomaly Detection in Real-Time Surveillance

Phan, D. T., Doan, V. H. M., Choi, J., Lee, B.-I., & Oh, J. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551832 · Cited by 17 · see citing works

Boost-Up Efficiency of Defective Solar Panel Detection With Pre-Trained Attention Recycling

Park, Y., Kim, M. J., Gim, U.-J., & Yi, J. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3255227 · Cited by 16 · see citing works

MemFormer: A memory based unified model for anomaly detection on metro railway tracks

Liu, R., Liu, W., Duan, M., Xie, W., Dai, Y., & Liao, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121509 · Cited by 16 · see citing works

SaliencyCut: Augmenting plausible anomalies for anomaly detection

Ye, J., Hu, Y., Yang, X., Wang, Q., Huang, C., & Huang, K. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110508 · Cited by 15 · see citing works

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

Im, J., Son, Y., & Hong, J. H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00915 · Cited by 15 · see citing works

Deep feature clustering for multi-class industrial image anomaly detection

Wang, R., Li, Z., Zheng, L., Wang, W., & Li, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113134 · Cited by 14 · see citing works

Combined Anomaly Aware Weakly Supervised Lightweight Model for Surface Defect Inspection

Zhang, Z., Shang, Z., Wang, X., & Ma, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3348835 · Cited by 14 · see citing works

Stones From Other Hills: Intrusion Detection in Statistical Heterogeneous IoT by Self-Labeled Personalized Federated Learning

Lu, W., Ye, A., Xiao, P., Liu, Y., Yang, L., Zhu, D., & Liu, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3526379 · Cited by 14 · see citing works

Few-Sample Anomaly Detection in Industrial Images With Edge Enhancement and Cascade Residual Feature Refinement

Yao, N., Zhao, Y., Guo, Y., & Kong, S. G. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3438261 · Cited by 13 · see citing works

A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection

Huang, J., Ariffin, S. A., Chen, Y., Lin, J., & Xu, W. (2025) · IEEE Access · doi:10.1109/access.2025.3529701 · Cited by 13 · see citing works

UniFormaly: Towards task-agnostic unified framework for visual anomaly detection

Lee, Y., Lim, H., Jang, S., & Yoon, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111820 · Cited by 13 · see citing works

Anomaly Detection for Medical Images Using Teacher–Student Model With Skip Connections and Multiscale Anomaly Consistency

Liu, M., Jiao, Y., Lü, J., & Chen, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3406792 · Cited by 12 · see citing works

A Partially Labeled Anomaly Data Detection Approach Based on Prioritized Deep Reinforcement Learning for Consumer Electronics Security

Qin, S., Liu, S., Ye, S., Fan, X., Cheng, M., He, Y., Deng, X., & Park, J. H. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3445629 · Cited by 11 · see citing works

Cross-Attention-Based Saliency Inference for Predicting Cancer Metastasis on Whole Slide Images

Su, Z., Rezapour, M., Sajjad, U., Niu, S., Gurcan, M. N., & Niazi, M. K. K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3439499 · Cited by 10 · see citing works

No label left behind: a unified surface defect detection model for all supervision regimes

Rolih, B., Fučka, M., & Skočaj, D. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02680-8 · Cited by 10 · see citing works

Multiscale Prototype Fusion Network for Industrial Product Surface Anomaly Detection and Localization

Shao, H., Peng, J., Shao, M., & Liu, B. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3450749 · Cited by 10 · see citing works

Semi-supervised noise-resilient anomaly detection with feature autoencoder

Zhu, T., Liu, L., Sun, Y., Lü, Z., Zhang, Y., Xu, C., & Chen, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112445 · Cited by 9 · see citing works

A Unified Latent Schrödinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization

Akshay, S., Narasimhan, N. L., George, J., & Balasubramanian, V. N. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02377 · Cited by 9 · see citing works

Diffusion-based vision-language model for zero-shot anomaly detection in medical images

Chen, Y., Tao, H., Yang, Z., Cao, Y., Jiang, C., Hu, L., Xiong, P., & Qiu, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112181 · Cited by 9 · see citing works

Training-Free Industrial Defect Generation with Diffusion Models

Xu, R., Chiu, Y.-T., Chen, T.-I., Chew, O., Chuang, Y., & Cheng, W.-H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02244 · Cited by 9 · see citing works

A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method

Chen, Y., Peng, H., Huang, L., Zhang, J., & Jiang, W. (2023) · IEEE Access · doi:10.1109/access.2023.3332475 · Cited by 10 · see citing works

SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image

Jin, Q., Jiang, Y., Lu, X., Liu, Y., Chen, Y., Gao, D., Sun, Q. L., & Zhuo, C. (2024) · doi:10.1145/3676536.3676752 · Cited by 8 · see citing works

Accurate industrial anomaly detection with efficient multimodal fusion

Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Duong, T. H. A., Huynh, T.-M., Nguyen, D.-M., Cao, M.-D., Ngo, D., Nguyen, T.-U., Phan, K.-T., et al. (2025) · Array · doi:10.1016/j.array.2025.100512 · Cited by 8 · see citing works

Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets

Qiu, J. W., Shi, H., Hu, Y. H., & Yu, Z. (2023) · Sensors · doi:10.3390/s23239360 · Cited by 8 · see citing works

Patch Matching for Few-Shot Industrial Defect Detection

Chen, R., Yu, G., Qin, Z., Song, K., Tu, J., Jiang, X., Liang, D., & Peng, C. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3413170 · Cited by 8 · see citing works

Harnessing federated learning for anomaly detection in supercomputer nodes

Farooq, E., Milano, M., & Borghesi, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.052 · Cited by 8 · see citing works

Feature Consistency Learning for Anomaly Detection

Li, H., & Hu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522399 · Cited by 8 · see citing works

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

Wang, F., Zhang, T., Wang, Y., Qiu, Y., Liu, X., Guo, X., & Cui, Z. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01901 · Cited by 7 · see citing works

CLIP-MDC: CLIP encoder based multimodal defect classification with synthetic anomaly generation for real-time surface defect detection

Ha, T., Hwang, C., & Jeong, J. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02773-4 · Cited by 7 · see citing works

Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining

He, H., Erfani, S., Gong, M., & Ke, Q. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00115 · Cited by 7 · see citing works

Detecting energy theft with partially observed anomalies

Chen, H., Ma, R., Liu, X., & Liu, R. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110323 · Cited by 7 · see citing works

CFOR: Character-First Open-Set Text Recognition via Context-Free Learning

Liu, C., Yang, C. H., Fang, Z., Qin, H., & Yin, X.-C. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3480711 · Cited by 7 · see citing works

EYOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX

Tang, R., Sun, H., Liu, D., Xu, H. F., Qi, M., & Kong, J. (2023) · Applied Sciences · doi:10.3390/app13031506 · Cited by 7 · see citing works

MDGAD: Meta domain generalization for distribution drift in anomaly detection

Zhao, S., Yu, Z., Marbach, T. G., Wang, G., Yin, A., Zhou, Y., & Liu, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126483 · Cited by 7 · see citing works

Quantile-Based Maximum Likelihood Training for Outlier Detection

Taghikhah, M., Kumar, N., V, Šegvić, S., Eslami, A., & Gumhold, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30159 · Cited by 7 · see citing works

A simple and reliable semi-supervised anomaly detection network for detecting crack in stamped parts

Dong, X., Zhang, C., Liu, S., & Wang, D. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104301 · Cited by 6 · see citing works

MadCLIP: Few-Shot Medical Anomaly Detection with CLIP

Shiri, M., Beyan, C., & Murino, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_40 · Cited by 6 · see citing works

LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

Zhao, Y. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00406 · Cited by 6 · see citing works

Localize-diffusion based dual-branch anomaly detection

Jiang, J., Liu, X., Yan, P., Wei, S., & Cui, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107439 · Cited by 6 · see citing works

Robustness of unsupervised methods for image surface-anomaly detection

Božič, J., Fučka, M., Zavrtanik, V., & Skočaj, D. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01477-y · Cited by 5 · see citing works

SplatPose+: Real-Time Image-Based Pose-Agnostic 3D Anomaly Detection

Liu, Y., Hu, Y. S., Chen, Y., & Zelek, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_24 · Cited by 5 · see citing works

Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection

Baitieva, A., Bouaouni, Y., Briot, A., Ameln, D., Khalfaoui, S., & Akçay, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00386 · Cited by 5 · see citing works

ZSCIAD: a preliminary exploration of zero-shot class incremental anomaly detection method

Guo, Y., Gao, X., & Yin, J. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02682-6 · Cited by 5 · see citing works

Long-tailed detection and classification of wafer defects from scanning electron microscope images robust to diverse image backgrounds and defect scales

Park, T., Son, Y., Moon, S., Han, S., & Hong, J. H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112342 · Cited by 5 · see citing works

Scene-Adaptive SVAD Based On Multi-modal Action-Based Feature Extraction

Gao, S., Yang, P., & Huang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0908-6_19 · Cited by 5 · see citing works

Semantics-Adaptive Multitask Defect Detection in Vacuum Packaging

Yang, L., Zhang, J., Quan, X., Lian, C., Lv, X., Li, L., Si, B., & Zhao, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3561444 · Cited by 5 · see citing works

AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection

Koshil, M., Wegener, T., Mentrup, D., Frintrop, S., & Wilms, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_19 · Cited by 5 · see citing works

Frequency domain nuances guided parallel transformer model for industrial anomaly localization

Zhao, J., Yu, K., Miao, Y., Wang, Y., Ma, Y., Zhang, J., Zhao, J., Qiang, Y., & Pei, B. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109852 · Cited by 5 · see citing works

Unsupervised Brain MRI Anomaly Detection via Inter-Realization Channels

Madni, H. A., Shujat, H., Nardin, A. D., Zottin, S., & Foresti, G. L. (2025) · International Journal of Neural Systems · doi:10.1142/s0129065725500479 · Cited by 5 · see citing works

GlobalCLIP: Zero-shot manufacturing anomaly detection with adaptive self-cyclic emsemble learning

Shen, H., Zio, E., Xiong, J., & Ma, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129448 · Cited by 4 · see citing works

ISP-AD: a large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects

Krassnig, P. J., & Gruber, D. P. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02778-z · Cited by 5 · see citing works

CapNet: Learning Insulator Self-Blast From Bounding Box

Jiang, D., Cao, Y. J., & Yang, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3436123 · Cited by 4 · see citing works

Weakly Supervised Anomaly Detection by Utilizing Incomplete Anomaly Information

Fang, Q., & Su, Q. (2024) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1109/acai63924.2024.10899497 · Cited by 4 · see citing works

Advancements in AI-Based Anomaly Detection for Smart Manufacturing

Islam, M. R., & Farid, F. A. (2025) · Springer series in advanced manufacturing · doi:10.1007/978-3-031-80154-9_3 · Cited by 4 · see citing works

Open set text recognition technology

Yang, C. H., Liu, C., Fang, Z., Han, Z., Liu, C., & Yin, X.-C. (2023) · Journal of Image and Graphics · doi:10.11834/jig.230018 · Cited by 4 · see citing works

Robust Fraud Detection via Supervised Contrastive Learning

Vinay, M. S., Yuan, S., & Wu, X. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386925 · Cited by 3 · see citing works

ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection

Zoghlami, F., Bazazian, D., Masala, G. L., Gianni, M., & Khan, A. (2024) · IEEE Access · doi:10.1109/access.2024.3502514 · Cited by 3 · see citing works

Dual Memory Networks Guided Reverse Distillation for Unsupervised Anomaly Detection

Tran, C., Pham, L. H., Tran, D. N., Ho, Q., & Jeon, J. W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0960-4_22 · Cited by 3 · see citing works

A Two-Stage Deep-Learning Framework for Industrial Anomaly Detection: Integrating Small-Sample Semantic Segmentation and Knowledge Distillation

Guo, L., & Lv, F. (2025) · Machines · doi:10.3390/machines13080712 · Cited by 3 · see citing works

Anomaly Detection and Segmentation in Carotid Ultrasound Images Using Hybrid Stable AnoGAN

Saboori, A., Öhberg, F., Näslund, U., & Grönlund, C. (2025) · IEEE Access · doi:10.1109/access.2025.3611327 · Cited by 3 · see citing works

AnomalyControl: Highly-Aligned Anomalous Image Generation with Controlled Diffusion Model

Duan, Y., Xu, W., Wu, Q., Xie, G.-S., Zhao, F., & Shan, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755274 · Cited by 3 · see citing works

Deviation capture networks for anomaly detection

Yan, F., Cheng, J., Feng, J., Lu, Y., Zhang, W., Jiang, X., & Xu, M. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103886 · Cited by 3 · see citing works

A zero-shot anomaly detection network with patch-augmented prompts and test-time adaptation

Zhu, H., Zhao, C., Yuan, Y., & Liu, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113525 · Cited by 3 · see citing works

Multistage latent residual based anomaly detection

Xu, F., & Shao, Y. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04872-4 · Cited by 2 · see citing works

Anomaly Heterogeneity Learning for Open-Set Supervised Anomaly Detection

Zhu, J., Ding, C., Tian, Y., & Pang, G. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01668 · Cited by 44 · see citing works

Explainable Deep Few-shot Anomaly Detection with Deviation NetworksPreprint

Pang, G., Ding, C., Shen, C., & van den Hengel, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00462 · Cited by 47 · see citing works

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

Wang, F., Zhang, T., Wang, Y., Qiu, Y., Liu, X., Guo, X., & Cui, Z. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01901 · Cited by 7 · see citing works

Toward supervised anomaly detection

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