Informative knowledge distillation for image anomaly segmentation

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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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Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

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A survey of deep learning for industrial visual anomaly detection

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PNI: Industrial Anomaly Detection using Position and Neighborhood Information

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Complementary pseudo multimodal feature for point cloud anomaly detection

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Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping

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CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-Shot Anomaly Detection

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BMAD: Benchmarks for Medical Anomaly Detection

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Exploring plain ViT features for multi-class unsupervised visual anomaly detection

Zhang, J., Chen, X., Wang, Y., Wang, C., Liu, Y., Li, X., Yang, M., & Tao, D. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104308 · Cited by 117

A comprehensive survey for real-world industrial surface defect detection: Challenges, approaches, and prospects

Cheng, Y., Cao, Y., Yao, H., Luo, W., Jiang, C., Zhang, H., & Shen, W. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.11.022 · Cited by 78

Dual-Attention Transformer and Discriminative Flow for Industrial Visual Anomaly Detection

Yao, H., Luo, W., Yu, W., Zhang, X., Qiang, Z., Luo, D., & Shi, H. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3322156 · Cited by 69

Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection

Cao, Y., Xu, X., Cheng, Y., Sun, C., Du, Z., Gao, L., & Shen, W. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3536165 · Cited by 63

A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection

Jiang, Y., Cao, Y., & Shen, W. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110982 · Cited by 52

AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection

Wu, Q., Li, H., Tian, C., Wen, L., & Li, X. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.001 · Cited by 48

LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

Zhang, Y., Cao, Y., Xu, X., & Shen, W. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3468464 · Cited by 57

REB: Reducing biases in representation for industrial anomaly detection

Lyu, S., Mo, D., & Wong, W. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111563 · Cited by 38

Incremental Template Neighborhood Matching for 3D anomaly detection

Wang, J., Wang, X., Hao, R., Yin, H., Huang, B., Xu, X., & Liu, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127483 · Cited by 37

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

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Two-stage reverse knowledge distillation incorporated and Self-Supervised Masking strategy for industrial anomaly detection

Tong, G., Li, Q., & Song, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110611 · Cited by 36

BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization

Cao, Y., Xu, X., Sun, C., Gao, L., & Shen, W. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3344383 · Cited by 35

Enhanced Multi-Scale Features Mutual Mapping Fusion Based on Reverse Knowledge Distillation for Industrial Anomaly Detection and Localization

Tong, G., Li, Q., & Song, Y. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3350539 · Cited by 30

Attention-Gate-based U-shaped Reconstruction Network (AGUR-Net) for color-patterned fabric defect detection

Zhang, H., Wang, S., Lu, S., Yao, L., & Hu, Y. (2023) · Textile Research Journal · doi:10.1177/00405175221149450 · Cited by 28

A Survey of Methods for Automated Quality Control Based on Images

Diers, J., & Pigorsch, C. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01822-w · Cited by 29

Yolo-sd: simulated feature fusion for few-shot industrial defect detection based on YOLOv8 and stable diffusion

Wen, Y., & Wang, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02175-7 · Cited by 25

Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection

Liu, X., Wang, J., Leng, B., & Zhang, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681669 · Cited by 32

Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection

Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22

Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection

Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Tran, D.-T., Duong, V.-H., Mai, A.-T., Pham, D.-L., Phan, K.-T., Do, M.-Q., Duong, T. H. A., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3525567 · Cited by 22

Unsupervised anomaly detection and localization with one model for all category

Tan, P., & Wong, W. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111533 · Cited by 22

MCAD: Multi-classification anomaly detection with relational knowledge distillation

Li, Z., Ge, Y., Yue, X., & Meng, L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09838-0 · Cited by 23

Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection

Jiang, Y., Cao, Y., & Shen, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3463495 · Cited by 21

Position Encoding Enhanced Feature Mapping for Image Anomaly Detection

Wan, Q., Cao, Y., Gao, L., Shen, W., & Li, X. (2022) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49997.2022.9926547 · Cited by 21

Fusion-restoration model for industrial multimodal anomaly detection

Wang, J., Niu, Y., & Huang, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130073 · Cited by 18

Dual-path Frequency Discriminators for few-shot anomaly detection

Bai, Y., Zhang, J., Chen, Z., Dong, Y., Cao, Y., & Tian, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112397 · Cited by 20

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

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Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

Yao, H., Luo, W., Zhang, W., Zhang, X., Qiang, Z., & Luo, D. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109235 · Cited by 19

Model compression optimized neural network controller for nonlinear systems

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LightCTS*: Lightweight Correlated Time Series Forecasting Enhanced With Model Distillation

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Fast and efficient computing for deep learning-based defect detection models in lightweight devices

Fișne, A., Kalay, A., & Eken, S. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02487-z · Cited by 14

Dual-student knowledge distillation for visual anomaly detection

Hao, J., Huang, K., Chen, C., & Mao, J. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01412-4 · Cited by 13

MGSGNet-S*: Multilayer Guided Semantic Graph Network via Knowledge Distillation for RGB-Thermal Urban Scene Parsing

Zhou, W., Wu, H., & Jiang, Q. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3456437 · Cited by 13

Unsupervised industrial anomaly detection using paired well-lit and low-light images

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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

Semi-supervised pipeline anomaly detection algorithm based on memory items and metric learning

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A novel anomaly detection and classification algorithm for application in tuyere images of blast furnace

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Self-Supervised Autoencoders for Visual Anomaly Detection

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Skip-ST: Anomaly Detection for Medical Images Using Student-Teacher Network with Skip Connections

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InViT: GAN Inversion-Based Vision Transformer for Blind Image Inpainting

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A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model

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Deep Feature Contrasting for Industrial Image Anomaly Segmentation

Wan, Q., Cao, Y., Gao, L., Li, X., & Gao, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3348901 · Cited by 9

Test Time Training for Industrial Anomaly Segmentation

Costanzino, A., Ramirez, P. Z., Moro, M. D., Aiezzo, A., Lisanti, G., Salti, S., & Stefano, L. D. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00395 · Cited by 9

Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection

Aqeel, M., Sharifi, S., Cristani, M., & Setti, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_28 · Cited by 10

Adaptive Context-Aware Distillation for Industrial Image Anomaly Detection

He, Y. J., Yang, H. S., & Yin, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336758 · Cited by 8

AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting

Ali, M., Fioraio, N., Salti, S., & Stefano, L. D. (2024) · IEEE Access · doi:10.1109/access.2024.3520002 · Cited by 8

Industrial anomaly detection with multiscale autoencoder and deep feature extractor‐based neural network

Tang, T., Hsu, H., & Li, K. (2023) · IET Image Processing · doi:10.1049/ipr2.12752 · Cited by 7

3D multimodal feature for infrastructure anomaly detection

Jing, Y., Lin, W., Sheil, B. B., & Acikgoz, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106388 · Cited by 7

KD-KI: Knowledge distillation with knowledge infusion for anomaly detection and localization

Huang, W., Xu, Z., Wan, R., Yang, X.-H., & Zhang, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131423 · Cited by 8

MGSCA: Multiscale Gradient Selection-Based Coupled-Hypersphere Adaptation for Unsupervised Pavement Defect Detection

Zhang, Y., Li, Z., Lai, T., Li, W., & Zheng, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3370756 · Cited by 6

Unsupervised anomaly detection and localization via bidirectional knowledge distillation

Wang, X., Wang, Y., Pan, Z., & Wang, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10172-8 · Cited by 7

MiniMaxAD: A lightweight autoencoder for feature-rich anomaly detection

Wang, F., Liu, C., Shi, L., & Pang, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104315 · Cited by 9

Improving generalization in deep neural network using knowledge transformation based on fisher criterion

Morabbi, S., Soltanizadeh, H., Mozaffari, S., & Fadaeieslam, M. J. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05448-0 · Cited by 6

Robust AD: A Real World Benchmark Dataset for Robustness in Industrial Anomaly Detection

Pemula, L., Zhang, D., & Dabeer, O. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00389 · Cited by 5

CFFDist: Cross-Scale Feature Fusion Distillation Network for Industrial Anomaly Localization

Zhi, H., Qin, H., Zhang, L., Guo, J., & Song, B. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3502773 · Cited by 5

Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation

Li, J., Li, M., Huang, S., Wang, G., & Zhao, X. (2025) · Sensors · doi:10.3390/s25123721 · Cited by 4

A novel dual-student reverse knowledge distillation method for magnetic tile defect detection

Tang, J., Zhang, A., & Liu, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-12339-2 · Cited by 3

A feature shuffling and restoration strategy for universal unsupervised anomaly detection

Luo, W., Yao, H., Qiang, Z., Zhang, X., & Zhang, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114874 · Cited by 3

Masked feature regeneration based asymmetric student–teacher network for anomaly detection

Gu, H., Li, G., & Liu, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18512-5 · Cited by 4

Domain knowledge-driven encoder–decoder for nasopharyngeal carcinoma segmentation

Xu, G.-X., Ren, C.-X., & Sun, Y. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125208 · Cited by 3

Outlier Exposure in Visual Industrial Anomaly Detection - Survey

Shi, Y. H., Xu, R., & Qi, Z. (2024) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw65004.2024.00008 · Cited by 4

Discriminating feature ratio: Introducing metric for uncovering vulnerabilities in deep convolutional neural networks

Szandała, T., & Maciejewski, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112306 · Cited by 4

Teacher Probability Reconstruction based knowledge distillation within intelligent network compression

Chen, H., Teng, X., Su, J., Li, C., Hu, C., & Han, M. (2025) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2025.02.001 · Cited by 3

High-Resolution Image Anomaly Detection via Spatiotemporal Consistency Incorporated Knowledge Distillation

Cao, Y., Zhang, Y., & Shen, W. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260338 · Cited by 3

Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection

Yao, H., Luo, W., Cao, Y., Zhang, Y., Yu, W., & Shen, W. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3595539 · Cited by 4

Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts

Hou, Y., Xu, K., Li, J., Ruan, Y., & Qiu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_4 · Cited by 3

Unsupervised Anomaly Detection on Preclinical Liver H &E Whole Slide Images Using Graph Based Feature Distillation

Li, L., Shelton, L. E., Forest, T. W., Janardhan, K. S., Jenkins, T., Napolitano, M. J., Wang, R., Leigh, D. A., Shah, T., Carlson, G. E., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_64 · Cited by 2

SD-GASNet: Efficient Dual-Domain Multi-Scale Fusion Network with Self-Distillation for Surface Defect Detection

Fu, J., Zhang, Z., Peng, T., Hu, X., & Zhang, J. (2025) · Sensors · doi:10.3390/s26010023 · Cited by 2

IAENet: An importance-aware ensemble model for 3D point cloud-based anomaly detection

Cao, X., Tao, C., Cheng, Y., & Du, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104097 · Cited by 2

A 3D anomaly detection method based on multiscale point features for defects in vehicle stamped parts

Liu, Y., Zhang, C., Dong, X., & Yang, Y. (2026) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2026.01.102 · Cited by 2

RoCA: Robust Contrastive Adaptation for unsupervised anomaly detection

Tan, X., Chen, C., Gao, H., Yu, Y., & Liang, R. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113843 · Cited by 2

Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application

Yu, R., Guo, B., & Li, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02095 · Cited by 2

3D-FPFH-Int: a hybrid geometric–radiometric descriptor for structural surface anomaly detection in tropical heritage

Gbran, H., & Murtiono, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-59034-4 · Cited by 2

Unsupervised Prediction of Blastocyst Development from Oocyte Images

García-de-la-Puente, N. P., Payá, E., Murria, L., López-Pérez, M., Meseguer, M., & Naranjo, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78241-1_19 · Cited by 2

Abnormal-Aware Loss and Full Distillation for Unsupervised Anomaly Detection Based on Knowledge Distillation

Zhao, M., & Song, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222157 · Cited by 2

Unsupervised industry anomaly detection via asymmetric reverse distillation

Sun, X., Pan, W., Qin, J., Lang, Y., & Qian, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109759 · Cited by 2

Integrating statistical significance and discriminative power in pattern discovery

Alexandre, L., Costa, R. S., & Henriques, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113356 · Cited by 2

Out-of-distribution detection in 3D applications: A review

Li, Z., Kang, X., West, J., & Khoshelham, K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134503 · Cited by 2

Learning to Be a Transformer to Pinpoint Anomalies

Costanzino, A., Ramirez, P. Z., Lisanti, G., & Stefano, L. D. (2025) · IEEE Access · doi:10.1109/access.2025.3582900 · Cited by 1

A Hierarchical Patch Feature Distribution Network for Industrial Multiscale Defect Detection

Zheng, Z., Yang, H. S., & Yin, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3598017 · Cited by 1

Few-shot anomaly detection and classification through reinforced data selection with a combinatorial reward

Han, X., Xu, D., Yuan, S., & Wu, X. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02572-6 · Cited by 1

UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion

Li, Z., Lin, G., Zhang, D., Huang, R., & Yang, J. (2026) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-026-00215-3 · Cited by 1

Few-shot medical anomaly detection through centroid consultation back and test-time self-calibration

Nie, Z., Xu, M., Cui, Y., Wei, H., Yi, W., Niu, S., Wan, Y., Wei, X., & Song, W. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113261 · Cited by 1

Bidirectional adaptive transformers for multimodal anomaly detection

Jiang, Y., Cao, Y., & Shen, W. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132129 · Cited by 1

Enhanced Reverse Distillation Guided Segmentation Network for Anomaly Detection

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