Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization

Momentum Contrast for Unsupervised Visual Representation Learning

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137 · see citing works

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203 · see citing works

Support Vector Method for Novelty Detection

Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J. S., & Platt, J. C. (1999) · Cited by 2,038 · see citing works

Deep One-Class Classification

Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works

PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

Defard, T., Setkov, A., Loesch, A., & Audigier, R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68799-1_35 · Cited by 1,415 · see citing works

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00954 · Cited by 1,107 · see citing works

A Unifying Review of Deep and Shallow Anomaly Detection

Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,080 · see citing works

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990 · see citing works

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

Yi, J., & Yoon, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-69544-6_23 · Cited by 551 · see citing works

Deep Anomaly Detection Using Geometric TransformationsPreprint

Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479 · see citing works

VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., & Foresti, G. L. (2021) · 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576231 · Cited by 462 · cited 2× · see citing works

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Cohen, N., & Hoshen, Y. (2020) · arXiv · doi:10.48550/arxiv.2005.02357 · Cited by 372 · see citing works

Explainable Deep One-Class Classification

Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., & Müller, K.-R. (2020) · arXiv · doi:10.48550/arxiv.2007.01760 · Cited by 185 · see citing works

Deep Nearest Neighbor Anomaly DetectionPreprint

Bergman, L., Cohen, N., & Hoshen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10445 · Cited by 154 · see citing works

Learning and Evaluating Representations for Deep One-class ClassificationPreprint

Sohn, K., Li, C., Yoon, J., Jin, M., & Pfister, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02578 · Cited by 138 · see citing works

Deep Industrial Image Anomaly Detection: A Survey

Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463 · see citing works

MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

Yang, M., Wu, P., & Feng, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105835 · Cited by 257 · see citing works

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

Tao, X., Gong, X., Zhang, X., Yan, S., & Adak, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196436 · Cited by 228 · see citing works

Registration Based Few-Shot Anomaly Detection

Huang, C., Guan, H., Jiang, A., Zhang, Y., Spratling, M., & Wang, Y.-F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20053-3_18 · Cited by 216 · see citing works

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

Cui, Y., Liu, Z., & Lian, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282993 · Cited by 164 · see citing works

A survey of deep learning for industrial visual anomaly detection

Li, Z., Yan, Y., Wang, X., Ge, Y., & Meng, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11287-7 · Cited by 145 · see citing works

Prototypical Residual Networks for Anomaly Detection and Localization

Zhang, H., Wu, Z., Wang, Z., Chen, Z., & Jiang, Y. (2023) · 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/cvpr52729.2023.01562 · Cited by 126 · see citing works

FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction

Fang, Z., Wang, X., Li, H., Liu, J., Hu, Q., & Xiao, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01603 · Cited by 118 · see citing works

Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

Luo, W., Cao, Y., Yao, H., Zhang, X., Lou, J., Cheng, Y., Shen, W., & Yu, W. (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/cvpr52734.2025.00932 · Cited by 65 · see citing works

Template-guided Hierarchical Feature Restoration for Anomaly Detection

Guo, H., Ren, L., Fu, J., Wang, Y., Zhang, Z., Lan, C., Wang, H., & Hou, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00593 · Cited by 54 · see citing works

MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

Bao, T., Chen, J., Li, W., Wang, X., Fei, J., Wu, L., Zhao, R. J., & Ye, Z. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00106 · Cited by 40 · see citing works

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

Li, H., Hu, J., Li, B., Chen, H., Zheng, Y., & Shen, C. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3448263 · Cited by 39 · see citing works

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 · see citing works

U-Flow: A U-Shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold

Tailanián, M., Pardo, Á., & Musé, P. (2024) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-024-01193-y · Cited by 34 · 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

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

Simple and effective Frequency-aware Image Restoration for industrial visual anomaly detection

Liu, T., Li, B., Du, X., Jiang, B., Geng, L., Wang, F., & Zhao, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103064 · Cited by 63 · see citing works

Anomaly Detection using Score-based Perturbation Resilience

Shin, W., Lee, J., Lee, T.-H., Lee, S., & Yun, J. P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02136 · Cited by 26 · see citing works

FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization

Jiang, Z., Zhang, Y., Wang, Y., Li, J., & Gao, X. (2024) · Sensors · doi:10.3390/s24051368 · Cited by 25 · see citing works

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 26 · see citing works

Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation

Zhang, F., Zhu, H., Cen, Y., Kan, S., Zhang, L., Vadakkepat, P., & Lee, T. H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3420818 · Cited by 23 · see citing works

Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples

Pei, M.-J., Liu, N., Zhao, B., & Sun, H. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00328-0 · Cited by 22 · see citing works

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning

Huang, C., Guan, H., Jiang, A., Wang, Y.-F., Spratling, M., Wang, X., & Zhang, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3465446 · Cited by 20 · see citing works

Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization

Deng, S., Sun, Z., Zhuang, R., & Gong, J. (2023) · Applied Sciences · doi:10.3390/app132212436 · Cited by 17 · see citing works

Discriminative Feature Learning Framework With Gradient Preference for Anomaly Detection

Xu, M., Zhou, X., Gao, X., He, W., & Niu, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3228007 · Cited by 15 · see citing works

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection

Liang, Y., Hu, Z., Huang, J., Di, D., Su, A., & Fan, L. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3545987 · Cited by 15 · see citing works

GrainBrain: Multiview Identification and Stratification of Defective Grain Kernels

Fan, L., Fan, D., Ding, Y., Wu, Y. W., Di, D., Pagnucco, M., & Song, Y. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3528555 · Cited by 13 · see citing works

SIA-net: Structural information awareness network based on normal samples for surface defect detection

Ma, Q., Zhang, E., Chen, Y., Duan, J., & Shao, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107131 · Cited by 12 · see citing works

Self-Supervised Autoencoders for Visual Anomaly Detection

Bauer, A., Nakajima, S., & Müller, K. (2024) · Mathematics · doi:10.3390/math12243988 · Cited by 11 · see citing works

WARM: A wavelet adaptive restoration module for surface anomaly detection

Qiao, Z., Lin, M., Lin, J., & Ding, D. (2024) · Measurement · doi:10.1016/j.measurement.2024.114689 · Cited by 9 · see citing works

Few-shot anomaly detection with adaptive feature transformation and descriptor construction

Zhengnan, H. U., Xiangrui, Z. E. N. G., Li, Y., Yin, Z., Meng, E., Zhu, L., & Kong, X. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.06.007 · Cited by 9 · 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

DC-AD: A Divide-and-Conquer Method for Few-Shot Anomaly Detection

Zhang, J., Yang, Z., & Song, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111360 · Cited by 8 · see citing works

A multi-scale information fusion framework with interaction-aware global attention for industrial vision anomaly detection and localization

Li, Z., Ge, Y., & Meng, L. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103356 · Cited by 8 · see citing works

ADFormer: Generalizable Few-Shot Anomaly Detection With Dual CNN-Transformer Architecture

Zhu, B., Gu, Z., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522624 · Cited by 8 · see citing works

PNG: an adaptive local-global hybrid framework for unsupervised material surface defect detection

Sun, Q., Xu, K., Zhao, D., Li, H., Jin, L., Liu, C., & Xu, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128711 · Cited by 7 · see citing works

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 · see citing works

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models

Han, D., Xu, L., Zhou, M., Wan, J., Li, M., & Li, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106906 · Cited by 6 · see citing works

DAS3D: Dual-Modality Anomaly Synthesis for 3D Anomaly Detection

Li, K., Dai, B., Fu, J., & Hou, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_10 · Cited by 6 · see citing works

Self-supervised Augmented Patches Segmentation for Anomaly Detection

Long, J., Yang, Y., Hua, L., & Ou, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26284-5_6 · Cited by 6 · see citing works

JRCC-Net: A Segmentation Network With Joint Representation and Contrast Clustering for Surface Anomaly Detection

Zhang, R., Wang, H., Feng, M., Liu, Y., & Yang, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3295468 · Cited by 6 · see citing works

Learned Optical Flow-Based Defect Detection With Coarse-to-Fine Nonrigid Image Alignment

Deng, L., Meng, X., Guo, X., Chen, Y., Shan, H., Wu, S., Lin, J., Dong, M., & Zhu, L. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3530104 · Cited by 5 · see citing works

Efficient visual anomaly detection model with adaptive wavelet transform

Du, X., Li, B., Liu, T., Jin, L., Ding, Y., & Zhao, Z. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108457 · Cited by 5 · see citing works

Accelerating surface defect detection using normal data with an attention-guided feature distillation reconstruction network

Wang, H., He, X., Zhang, C., Liang, X., Zhu, P., Wang, X., Gui, W., Li, X., & Qian, X. (2025) · Measurement · doi:10.1016/j.measurement.2025.116702 · Cited by 5 · see citing works

AdapSyn: Anomaly detection based on triplet training with adaptive anomaly synthesis

Zhou, S., Lin, C., Chen, Z., Guo, B., & Zhao, Y. (2024) · Displays · doi:10.1016/j.displa.2024.102885 · 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

SegLGAD: Local-to-global industrial anomaly detection with visual segmentation model

Du, X., Li, B., Liu, T., Ding, Y., Jin, L., & Zhao, Z. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.112633 · Cited by 4 · see citing works

Zero-shot printed circuit board defect detection via optical flow and reconstruction guidance

Yin, X., Wang, S., Wang, Y., Wang, P., Liu, Y., Shen, T., & Qiao, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104355 · Cited by 4 · see citing works

Unsupervised Anomaly Detection and Segmentation on Dirty Datasets

Guo, J., Yu, X., & Wang, L. (2022) · Future Internet · doi:10.3390/fi14030086 · Cited by 4 · see citing works

LightFlow: Lightweight Unsupervised Defect Detection Based on 2-D Flow

Peng, C., Zhao, L., Wang, S., Abbas, Z., Liang, F., & Islam, M. S. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3415769 · Cited by 4 · see citing works

Automated defect detection on inductive thermography images using supervised and semi-supervised Deep Learning methods

Tout, K., Samet, N., & Bouteille, P. (2023) · e-Journal of Nondestructive Testing · doi:10.58286/28510 · Cited by 3 · see citing works

Cross‐class pest and disease vegetation detection based on small sample registration

Liu, J., Linfeng, W., Yunsheng, W., Mingming, A., Wenfei, J., & Xu, S. (2023) · IET Image Processing · doi:10.1049/ipr2.12779 · Cited by 2 · see citing works

Anodapter: A Unified Framework for Generating Aligned Anomaly Images and Masks Using Diffusion Models

Shin, M., Jeong, S.-G., & Heo, Y. S. (2025) · IEEE Access · doi:10.1109/access.2025.3568866 · Cited by 2 · see citing works

Task-oriented semantic communication towards industrial anomaly detection

Lin, C., Zhang, P., Wang, R., & Yang, Z. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107886 · Cited by 2 · see citing works

MemFlow-AD: An anomaly detection and localization model based on memory module and normalizing flow

Xie, X., Li, Z., Xiong, S., Liu, Z., & Cai, T. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104454 · Cited by 2 · see citing works

Insulator Surface Defect Detection Method Based on Graph Feature Diffusion Distillation

Li, S., Na, Z., Yang, G., Hou, Y., & Zhang, X. (2025) · Journal of Imaging · doi:10.3390/jimaging11060190 · Cited by 2 · see citing works

Anomaly Detection via a Bottleneck Structure Robust to Noise in In-Distribution Samples

Cho, Y., Lee, D. H., Hyun, Y., & Kim, W. (2024) · IEEE Access · doi:10.1109/access.2024.3400122 · Cited by 1 · see citing works

SPACE: SPAtial-Aware Consistency rEgularization for Anomaly Detection in Industrial Applications

Kim, D.-H., Kim, H., Jeong, D., Suh, S., & Cho, H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00698 · Cited by 1 · see citing works

DFTGL: Domain Filtered and Target Guided Learning for few-shot anomaly detection

Zhang, J., Song, Y., & Yang, Z. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104457 · Cited by 1 · see citing works

Research progress on key technologies for product surface defect detection based on empirical mode decomposition and deep learning

Xiong, J., Zhang, Q., Wang, Q., Yang, J., Dong, X., & Liang, Q. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16431-9 · Cited by 1 · see citing works

Prior-Guided Feature Sampling and Restoration for Few-Shot Industrial Anomaly Detection

Hu, X., & Yang, H. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3670014 · Cited by 1 · see citing works

Registration Based Feature Core-Set Model for Defect Detection

Jing, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450867 · Cited by 1 · see citing works

Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment

Zhang, Y., Jiang, Z., Peng, C. X., Zhu, X., & Wang, G. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.342094 · Cited by 1 · see citing works

FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

Han, D., Xu, L., Zhou, M., Li, G., & Li, M. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831932 · Cited by 1 · see citing works

An Anomaly Detection and Localization Method Based on Feature Fusion and Attention

Li, Z., Xie, X., Wu, D., Xiong, S., & Cai, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9788-6_19

Enhancing Anomaly Detection Performance in Manufacturing Industry through Improved Alignment Algorithms

Ban, H., Kim, D.-H., Kim, H., & Cho, H. (2024) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic61013.2024.10457237

Adapsyn: Anomaly Detection Based on Triplet Training with Adaptive Anomaly Synthesis (Chinamm2024)Preprint

Zhou, S., Lin, C., Chen, Z., Guo, B., & Zhao, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4889011

Real-Time Industrial Anomaly Detection via Sparse Reconstruction

Zhou, C., Wan, J., & Zhao, X. (2024) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar61438.2024.10670980

Few-Shot Industrial Anomaly Detection via Proxy Task Learning on Normal Samples

He, S., Xu, R., Huang, W., Chen, Y., & Wang, L. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00220

NLPA-AD: normal label propagation algorithm for zero-shot texture anomaly detection

Zhang, J., Song, Y., Yang, Z., & Wang, C. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06733-6

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 · see citing works

Research on Defect Detection of Wire Rope of Mine Hoist Based on Feature Embedding

Ma, T., Wang, J., Zhou, L., Li, J., Li, Y., & Yang, J. (2025) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc66358.2025.11200405

Semantic Reconstruction for Unified Detection of Local and Logical Anomalies

Koizumi, K., Hayami, T., & WATANABE, H. K. (2025) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce65946.2025.11275329

A defect detection method based on feature reconstruction using diffusion models

Chen, X.-F., Fu, F., & Fang, M. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-05037-z

ObjSegAD-Net: Region-aware pseudo-defect injection and dual-branch architecture for unsupervised industrial anomaly detection

Dong, S., Hu, F., Wei, B., & Wu, Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103707

Few-shot anomaly detection via adaptive graph feature registration

Wang, G., Xu, S., Li, K., & Ren, K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02678-x

Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems

Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818

Self-Supervised Anomaly Detection via Heatmap-Guided Pseudo-Anomaly GenerationPreprint

Cheng, N., Liu, W., Li, Q. S., & Wu, Y. N. (2026) · Research Square · doi:10.21203/rs.3.rs-9984996/v1

Fast Dynamic Prototypes for Unsupervised Anomaly Detection and Localization

Li, M., Li, H., Wu, L. Y., Liu, C., & Zhao, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37225-3_21

PTAD: Pose and Texture Agnostic Anomaly Detection

Zhuo, W., Xiang, J., Liu, M., & Lu, H. (2026) · doi:10.1109/cvprf71909.2026.00651

FeatDeNoiseNet: Multi-scale Structure-Consistent Manifold Modeling for Unsupervised Anomaly Localization

Fei, H., Qu, Z., Long, J., Hua, L., Qi, Q., & Luo, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3403-5_36

Image alignment-based patch distribution framework for anomaly detection

Liu, Y., Ma, L., & Jiang, H. (2024) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1117/12.3021499

Exploring Deep Learning-based Unsupervised Image Anomaly Detection and Localization Methods for Industrial Quality Assurance

Dutta, P., Nayak, R., & Pati, U. C. (2024) · International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) · doi:10.1109/ic-cgu58078.2024.10530676 · Cited by 2 · see citing works

Discriminative Feature Learning Framework With Gradient Preference for Anomaly Detection

Xu, M., Zhou, X., Gao, X., He, W., & Niu, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3228007 · Cited by 15 · see citing works

Unsupervised anomaly detection in industrial images via multi-scale attention fusion and strong-weak defect collaborative synthesis

Wang, Y., & Chai, L. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07183-4 · Cited by 1 · see citing works