Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection

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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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A Survey on Deep Transfer Learning

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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

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

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Deep One-Class Classification

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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning

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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

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Anomaly Detection with Robust Deep Autoencoders

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SmoothGrad: removing noise by adding noise

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Sanity Checks for Saliency Maps

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

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Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

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Adversarially Learned One-Class Classifier for Novelty Detection

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One-class SVM for learning in image retrieval

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GANomaly: Semi-supervised Anomaly Detection via Adversarial Training

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

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Latent Space Autoregression for Novelty Detection

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Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

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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

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 471 · see citing works

Deep Semi-Supervised Anomaly Detection

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Thoracic Disease Identification and Localization with Limited Supervision

Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L. (2018) · 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/cvpr.2018.00865 · Cited by 340 · see citing works

Deep Structured Energy Based Models for Anomaly Detection

Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334 · see citing works

Classification-Based Anomaly Detection for General DataPreprint

Bergman, L., & Hoshen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02359 · Cited by 294 · see citing works

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

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Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

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Attention Guided Anomaly Localization in Images

Venkataramanan, S., Peng, K.-C., Singh, R. V., & Mahalanobis, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_29 · Cited by 247 · see citing works

Loss Functions for Neural Networks for Image ProcessingPreprint

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OCTID: Optical coherence tomography image database

Gholami, P., Roy, P., Parthasarathy, M. K., & Lakshminarayanan, V. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106532 · Cited by 195 · see citing works

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

Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146 · see citing works

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based VisualizationsPreprint

Nie, W., Zhang, Y., & Patel, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07039 · Cited by 112 · see citing works

Iterative energy-based projection on a normal data manifold for anomaly localizationPreprint

Dehaene, D., Frigo, O., Combrexelle, S., & Eline, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03734 · Cited by 112 · see citing works

DROCC: Deep Robust One-Class ClassificationPreprint

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AVID: Adversarial Visual Irregularity Detection

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Transfer representation-learning for anomaly detection

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ARAE: Adversarially robust training of autoencoders improves novelty detection

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Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection

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Puzzle-AE: Novelty Detection in Images through Solving PuzzlesPreprint

Salehi, M., Eftekhar, A., Sadjadi, N., Rohban, M. H., & Rabiee, H. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12959 · Cited by 43 · see citing works

Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?Preprint

de Nazaré, T. S., de Mello, R. F., & Ponti, M. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08495 · Cited by 41 · see citing works

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder GradientsPreprint

Zimmerer, D., Petersen, J., Köhl, S., & Maier-Hein, K. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00003 · Cited by 21 · see citing works

Exploring Deep Anomaly Detection Methods Based on Capsule Net

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Head CT - hemorrhage

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Region-Based Anomaly Localisation in Crowded Scenes via Trajectory Analysis and Path Prediction

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Towards Total Recall in Industrial Anomaly Detection

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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

A Unified Model for Multi-Class Anomaly Detection

You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., & Le, X. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0330 · Cited by 303 · 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

A Diffusion-Based Framework for Multi-Class Anomaly Detection

He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., & Xie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28690 · Cited by 242 · see citing works

Informative knowledge distillation for image anomaly segmentation

Cao, Y., Wan, Q., Shen, W., & Gao, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108846 · Cited by 130 · see citing works

Deep One-Class Classification via Interpolated Gaussian Descriptor

Chen, Y., Tian, Y., Pang, G., & Carneiro, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19915 · Cited by 123 · see citing works

TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images

Liu, X., Liu, Q., Zhang, Y., Wang, M., & Tang, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106467 · Cited by 48 · see citing works

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 · see citing works

SLSG: Industrial image anomaly detection with improved feature embeddings and one-class classification

Yang, M., Liu, J., Yang, Z., & Wu, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110862 · Cited by 46 · see citing works

Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt

Liu, J., Wu, K., Nie, Q., Chen, Y., Gao, B.-B., Liu, Y., Wang, J., Wang, C., & Zheng, F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28153 · Cited by 48 · see citing works

Rethinking Reverse Distillation for Multi-Modal Anomaly Detection

Gu, Z., Zhang, J., Liu, L., Chen, X., Peng, J., Gan, Z., Jiang, G., Shu, A., Wang, Y., & Ma, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28687 · Cited by 37 · see citing works

An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder

Yang, Q., & Guo, R. (2024) · Sensors · doi:10.3390/s24082440 · Cited by 35 · see citing works

One-for-All: Proposal Masked Cross-Class Anomaly Detection

Yao, X., Zhang, C., Li, R., Sun, J., & Liu, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25604 · Cited by 35 · see citing works

A graph model-based multiscale feature fitting method for unsupervised anomaly detection

Zhang, F., Kan, S., Zhang, D., Cen, Y., Zhang, L., & Mladenovic, V. M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109373 · Cited by 33 · see citing works

AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

Wang, Z., Zhang, Y., Luo, L., & Wang, N. (2022) · Journal of Sensors · doi:10.1155/2022/3538541 · Cited by 26 · see citing works

Deep learning-based anomaly detection for precision field crop protection

Cheng, W., Shan, Y., & Zhen, M. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1576756 · Cited by 24 · see citing works

Two-stage coarse-to-fine image anomaly segmentation and detection model

Shah, R. A., Urmonov, O., & Kim, H. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104817 · Cited by 21 · see citing works

Fault anomaly detection method of aero-engine rolling bearing based on distillation learning

Kang, Y., Chen, G., Wang, H., Sheng, J., & Wei, X. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.11.034 · Cited by 21 · see citing works

Y-GAN: Learning dual data representations for anomaly detection in images

Ivanovska, M., & Štruc, V. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123410 · Cited by 21 · see citing works

Long-Tailed Anomaly Detection with Learnable Class Names

Ho, C.-H., Peng, K., & Vasconcelos, N. M. (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/cvpr52733.2024.01182 · Cited by 17 · see citing works

Image-based anomaly detection in low-light industrial environments with feature enhancement

Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Bui, S.-A., Duong, T. H. A., Huynh, T.-M., Nguyen, D.-M., Trinh, V.-A., Ha, Q. P., Long, N. V., et al. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104309 · Cited by 17 · see citing works

DMU-TransNet: Dense multi-scale U-shape transformer network for anomaly detection

Zhou, W., Wu, S., Wang, Y., Zuo, L., Yi, Y., & Cui, W. (2024) · Measurement · doi:10.1016/j.measurement.2024.114216 · Cited by 14 · see citing works

Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection

Fan, L., Ding, Y., Fan, D., Wu, Y. W., Pagnucco, M., & Song, Y. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230329 · Cited by 12 · see citing works

Transferable visual pattern memory network for domain adaptation in anomaly detection

Fan, C., Jin, Y., Liu, P., & Zhao, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106013 · Cited by 10 · see citing works

Unsupervised anomaly detection for manufacturing product images by significant feature space distance measurement

Shen, H., Wei, B., & Ma, Y. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111328 · Cited by 9 · see citing works

FAD: Feature augmented distillation for anomaly detection and localization

Zhong, Q., Qiu, X., Zhao, X., Huang, X., Liu, G., & Xiao, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128249 · Cited by 9 · see citing works

Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization

Jiang, H., Ji, S., He, G., & Li, X. (2023) · Scientific Programming · doi:10.1155/2023/2989533 · 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

Benchmarking framework for anomaly localization: Towards real-world deployment of automated visual inspection

Gangopadhyay, T., Hong, S., Roy, S., Shah, Y., & Cheong, L. L. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.05.024 · Cited by 7 · see citing works

Constrained Adaptive Projection with Pretrained Features for Anomaly Detection

Gui, X., Wu, D., Chang, Y. L., & Fan, S. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/286 · Cited by 7 · see citing works

A recover-then-discriminate framework for robust anomaly detection

Xing, P., Zhang, D., Tang, J., & Li, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4291-4 · Cited by 8 · see citing works

Relation-Based Knowledge Distillation for Anomaly Detection

Cheng, H., Yang, L., & Liu, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88004-0_9 · Cited by 6 · see citing works

Two-stage anomaly detection for positive samples and small samples based on generative adversarial networks

Xu, C., Ni, D., Wang, B. J., Wu, M., & Gan, H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14306-9 · Cited by 6 · see citing works

Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection

Gutierrez, P., Cordier, A., Caldeira, T., & Sautory, T. (2021) · Automated Visual Inspection and Machine Vision · doi:10.1117/12.2591876 · Cited by 6 · see citing works

A diverse embedding-based composite reconstruction encoder–decoder for color fabric defect detection

Zhang, H., Meng, L., Lu, S., Song, Z., & Wu, L. Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127261 · Cited by 5 · see citing works

Co-Progression Knowledge Distillation with Knowledge Prototype for Industrial Anomaly Detection

Yang, B., Zhang, Z., & Ma, J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i12.33419 · Cited by 5 · see citing works

Multi-input dual-branch reverse distillation for screw surface defect detection

Wang, X., Zheng, R., Zhu, J., Ji, Z., Zhang, M., & Wu, Q. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108920 · 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

Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

Shekarizadeh, S., Rastgoo, R., Al-Kuwari, S. M., & Sabokrou, M. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956722 · Cited by 5 · see citing works

Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

Lv, C., Zhang, Z., Shen, F., & Zhang, F. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00466 · Cited by 5 · see citing works

Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Luo, B., Zhu, J., Yang, T., Zhao, S., Hu, C., Zhao, X., & Gao, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25749 · Cited by 5 · see citing works

Using teacher-student neural networks based on knowledge distillation to detect anomalous samples in the otolith images

Chen, Y., & Zhu, G. (2023) · Zoology · doi:10.1016/j.zool.2023.126133 · Cited by 5 · see citing works

Causal Fusion of Convolutional Neural Network and Vision Transformer for Image Anomaly Detection and Localization

Zhang, S., Hu, X., & Liu, J. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687979 · Cited by 4 · 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

Revolutionizing Egg Quality Control: Advanced Prompt-based Models for Automated Detection of Broken Eggs Without the Need for Training

Soontornnapar, T., Praneenatthavee, N., & Ploysuwan, T. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-024-0471-6 · Cited by 4 · see citing works

Feature-adaptive anomaly detection model for onion inspection system

Song, Z., Buayai, P., Makino, K., & Mao, X. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100983 · Cited by 4 · see citing works

Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Khazaie, V. R., Wong, A., Jewell, J. T., & Mohsenzadeh, Y. (2022) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv55824.2022.00031 · Cited by 4 · see citing works

Texture and semantic convolutional auto‐encoder for anomaly detection and segmentation

Luo, J., Gao, C., Wan, D., & Shen, L. (2023) · IET Computer Vision · doi:10.1049/cvi2.12200 · Cited by 4 · see citing works

Multi-Modal Heterogeneous Encoder-Decoder for Alzheimer's Disease Anomaly Detection

Song, S., Bai, Z., Wang, G., Xu, Y., Cheng, M., & Chen, H. (2024) · International Conference on Intelligent Medicine and Health (ICIMH) · doi:10.1145/3715931.3715938 · Cited by 3 · see citing works

Knowledge Distillation for Anomaly Detection

Pol, A. A., Govorkova, E., Grönroos, S., Chernyavskaya, N., Harris, P. C., Maurizio Pierini, B., Ojalvo, I., & Elmer, P. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-159 · Cited by 3 · see citing works

RDMS: Reverse distillation with multiple students of different scales for anomaly detection

Chen, Z., Lyu, C., Zhang, L., Li, S., & Xia, B. (2024) · IET Image Processing · doi:10.1049/ipr2.13210 · Cited by 3 · see citing works

SAC-BL: A hypothesis testing framework for unsupervised visual anomaly detection and location

Ma, X., Wu, J., & Liu, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107147 · Cited by 3 · 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

Bipolar augmentation: Lightweight artificial intelligence model training for anomaly detection under color cast scenarios

Lin, Y., & Lin, Y.-M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110184 · Cited by 2 · see citing works

Teacher-student-based contrastive autoencoder for industrial process fault isolation with limited fault data

Jia, M., Xu, D., Yang, T., Liu, Y., & Gao, Z. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107899 · Cited by 1 · see citing works

MADViT: A vision transformer-based multilayer distillation framework for explainable anomaly detection in aerial imagery

Balwant, M. K., Mishra, S., & Misra, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129166 · Cited by 1 · see citing works

PAttL-GAN: Pixel Attention Localization Generative Adversarial Network for unsupervised anomaly detection in medical images

Li, G., Zou, Y., Liu, B., Jiang, N., Lv, J., Ai, W., Ji, D., Zhou, Q., & Zhang, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131345 · Cited by 1 · see citing works

Partial Distillation of Deep Feature for Unsupervised Image Anomaly Detection and Segmentation

Wan, Q., Gao, L., Wang, L., & Li, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84522-3_19 · Cited by 1 · see citing works

Clustering memory-guided anomaly detection model for large-scale screening of esophageal endoscopic images

Wu, Y., Zhao, Z., Pang, X., & Liu, J. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac073 · Cited by 1 · see citing works

Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

Wang, M., Xu, H., & Chen, Y. (2022) · Journal of New Media · doi:10.32604/jnm.2022.032447 · Cited by 1 · see citing works

Photometric stereo multi-information fusion unsupervised anomaly detection algorithm

Lan, J., & Shi, J. (2024) · Applied Optics · doi:10.1364/ao.524199 · Cited by 1 · see citing works

U-NeTrans at the Edge: Precision and Adaptability in Medical Image Analysis through Segment-based U-Net and Transformer IntegrationPreprint

Iqbal, S., Qureshi, A. N., Alhussein, M. A., Aurangzeb, K., & Wang, H. H. (2024) · Research Square · doi:10.21203/rs.3.rs-3955910/v1 · Cited by 1 · see citing works

Adcapsnet: A Novel Efficient and Robust Anomaly Detection Capsule Network Model for Deteriorated Iiot Sensor ImagesPreprint

Cai, X., Xiao, R., Zeng, Z., Gong, P., & Zhang, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4191163

基于特征金字塔匹配和自监督的表面缺陷检测

Ming, 梁. L., Minglu, 张. Z., & Lü, 吕. X. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop212927

Knowledge Distillation from Single to Multi Labels: An Empirical StudyPreprint

Liu, H., Zhang, Y., Qin, Y., & Gu, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4585915

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly ConsistencyPreprint

Liu, M., Jiao, Y., Lü, J., & Chen, H. (2023) · doi:10.36227/techrxiv.24330880

Mtddpm: Visual Anomaly Detection with Multiscale Denoising Diffusion Probabilistic ModelPreprint

Wang, X., & Li, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4632278

Saliencycut: Augmenting Plausible Anomalies for Anomaly DetectionPreprint

Ye, J., Hu, Y., Yang, X., Wang, Q., Huang, C., & Huang, K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4634960

Mtddpm: Visual Anomaly Detection with Multiscale Denoising Diffusion Probabilistic ModelPreprint

Wang, X., & Li, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4654306

Multi-scale Feature Imitation for Unsupervised Anomaly Localization

Hu, C., & Lai, S. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0855-0_49

Nn2vit: Neural Networks and Vision Transformers Based Approach for Visual Anomaly Detection in Industrial ImagesPreprint

Wahid, J. A., Ayoub, M., Xu, M., Jiang, X., Shi, L., & Hussain, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4811703

Anomaly detection algorithm for asymmetric autoencoder based on knowledge distillation

Liu, J., Wu, B., & Yu, L. (2024) · International Conference on Computer Graphics and Virtuality (ICCGV) · doi:10.1117/12.3029703

Nn2vit: Neural Networks and Vision Transformers Based Approach for Visual Anomaly Detection in Industrial ImagesPreprint

Wahid, J. A., Ayoub, M., Xu, M., Jiang, X., Shi, L., & Hussain, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4866728

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

A Cascaded Mini/Micro Led Defect Detection Method Based on Efficient Unsupervised Segmentation NetworkPreprint

Zhou, Y., Wang, W., Zeng, D., Huang, G., yu, C., & Wu, Z. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5031157

Semi-supervised bone marrow lesion detection from knee MRI segmentation using mask inpainting models

Qin, S., Zhang, M., Shan, J., Shin, T., Woo, J., & Xing, F. (2025) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3047033

Dscd: Dual-Student Collaborative Distillation for Industrial Anomaly DetectionPreprint

Ding, C., Xu, J., & Jiang, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5370573

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

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

The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review

Wittmann, A. M., Seeböck, P., Heger, K. A., Egger, D., Langs, G., & Waldstein, S. M. (2026) · Journal of Ophthalmology · doi:10.1155/joph/9792133

ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection

Lendering, C., Akdag, E., Figueira, J., & Bondarev, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37383-0_12

FastAD: A Deployable Framework for Real-Time Visual Anomaly Detection

Jing, Y., & Ding, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38401-0_22

UniNet++: A unified anomaly detection framework via Local-Global Feature Fusion and Gated Feature Selection

Wang, G., & Wang, Z. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117703

Distribution-Aligned Score Fusion and Max Pooling for Industrial Anomaly Detection under Domain Shift

翁, 长. (2026) · Journal of Sensor Technology and Application · doi:10.12677/jsta.2026.145083

LightViT-AD: lightweight vision transformer distillation for unsupervised UAV anomaly detection with real-time edge inference

Balwant, M. K., Mishra, S., & Misra, R. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03306-y

LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production

Carrilho, R., Rashidunnabi, M., & Proença, H. P. M. C. (2026) · Electronics · doi:10.3390/electronics15194403

Dual-branch anomaly-prompted SAM for OCT lesion segmentation with limited annotations

Dong, J., Huang, C., Qian, T., Kim, J., Bi, L., Wang, L., & Xu, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115053

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection

Chen, Q., Qu, Z., Luo, W., Yao, H., Cao, Y., Jiang, Y., Duan, Y., Luo, H., Lv, C., & Zhang, Z. (2026) · doi:10.1109/cvprf71909.2026.00821

ReMem: A Dynamic Memory Evolution Detector for Zero-Shot Anomaly Detection

Yi, L., Chen, Z., Wu, G., Ding, J., Wang, X., & Ning, Z. (2026) · doi:10.1109/cvprf71909.2026.00739

DA-CLIP: Mitigating Granularity Mismatch in Zero-Shot Anomaly Detection via Decoupled Text-Visual Alignment

Liu, J., Wang, P., Huang, J., Zhou, X., & Yu, L. (2026) · doi:10.1109/cvprf71909.2026.00582

DeStyle: A Unified Decoupled Framework for Style Transfer via Singular Value Purification and Attention-Guided Distillation

Liu, X., Xian, T., Geng, X., Zhao, D., & Liu, W. (2026) · Journal of Imaging · doi:10.3390/jimaging12100494

Behavioral Knowledge Distillation for Dynamic Graph Anomaly Detection in Trustworthy Power Grid Dispatching

Pang, Z., Gao, M., Han, Y., Wang, Q., Yan, W., Yao, S., & Gao, P. (2026) · Mathematics · doi:10.3390/math14203688

Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics

Ameyaw, S. A., & Ameyaw, J. A. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02353-9

A3AD: Enhancing CLIP with Triple-Aware Learning for Zero-Shot Anomaly Detection

Feng, A., Zheng, J., Yu, K., & Geng, C. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115125

Locality-aware transformer for multi-class industrial image anomaly detection

Wu, Y., Datago, A. M., & Tao, H. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02712-0

CDAD: A Multi-Scale Feature Fusion Model for Cross-Domain Anomaly Detection

Zhang, H., Wang, H., Jianyong, Z., & Wang, H. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729580

SEA-Prompt: Structured Expert Adaptation Prompting for Zero-Shot Anomaly Detection

Guo, H., Zhang, H., Yang, Y., Zhao, G., & Tang, J. (2026) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara71458.2026.11732555

CLIP-Based Adaptive Weighted Anomaly Detection with Cross-Layer Semantic Decoupling

Li, F., Wang, Y., Tao, X., Wei, M., Yang, Q., Li, J., Yuan, Z., & Yang, Y. (2026) · Applied Sciences · doi:10.3390/app162110499

Physics‐ and Statistics‐Guided Normal Subspace Purification for Training‐Free Few‐Shot Industrial Anomaly Detection

Li, X., Li, L., & Shao, X. (2026) · IET Image Processing · doi:10.1049/ipr2.70493

Feature Configuration Optimization of PaDiM for Industrial Anomaly Detection: An Experimental Study on Eight MVTec AD Categories

Zhao, N., Li, X., Chunxia, D., Chang, X., & Li, G. (2026) · Biomimetics · doi:10.3390/biomimetics11110776

A Refined Knowledge Distillation Network for Unsupervised Anomaly Detection

Han, Z., Wan, J., Li, Y., & Li, G. (2024) · Electronics · doi:10.3390/electronics13234793 · Cited by 3 · see citing works

Pull & Push: Leveraging Differential Knowledge Distillation for Efficient Unsupervised Anomaly Detection and Localization

Zhou, Q., He, S., Liu, H., Chen, T., & Chen, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3218587 · Cited by 66 · see citing works

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 24 · see citing works

D2AD: Diffusion Distillation for Unsupervised Image Anomaly Detection

Shao, Y., Ni, Z., & Liu, Q. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209101

Anomaly Detection and Localization via Reverse Distillation With Latent Anomaly Suppression

Wang, G., Zou, Y., He, S., Wang, Y., & Dai, R. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3562258 · Cited by 20 · see citing works