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

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Estimating the Support of a High-Dimensional Distribution

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168 · see citing works

Siamese Neural Networks for One-shot Image Recognition

Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,712 · see citing works

Support Vector Data Description

Tax, D. M. J., & Duin, R. P. W. (2003) · Machine Learning · doi:10.1023/b:mach.0000008084.60811.49 · Cited by 3,553 · see citing works

Colorful Image Colorization

Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,361 · see citing works

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works

Unsupervised Visual Representation Learning by Context Prediction

Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works

Unsupervised Representation Learning by Predicting Image Rotations

Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477 · 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

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

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. T. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007364503720380 · Cited by 819 · see citing works

Adversarially Learned One-Class Classifier for Novelty Detection

Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765 · see citing works

Revisiting Self-Supervised Visual Representation Learning

Kolesnikov, A., Zhai, X., & Beyer, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00202 · Cited by 675 · see citing works

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

Perera, P., Nallapati, R., & Xiang, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00301 · Cited by 608 · 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

Representation Learning by Learning to Count

Noroozi, M., Pirsiavash, H., & Favaro, P. (2017) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2017.628 · Cited by 379 · see citing works

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Facco, E., D’Errico, M. N., Rodríguez, Á., & Laio, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11873-y · Cited by 360 · see citing works

Towards Visually Explaining Variational Autoencoders

Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R. J., & Camps, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00867 · Cited by 294 · see citing works

A baseline for detecting misclassified and out-of-distribution examples in neural networksPreprint

Hendrycks, D. (2025) · arXiv (Cornell University) · doi:10.57702/9gs720u1 · Cited by 196 · see citing works

Intrinsic dimension of data representations in deep neural networksPreprint

Ansuini, A., Laio, A., Macke, J. H., & Zoccolan, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12784 · Cited by 187 · see citing works

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms

Vert, R., & Vert, J. (2006) · Cited by 141 · see citing works

Correction: Corrigendum: Intronless WNT10B-short variant underlies new recurrent allele-specific rearrangement in acute myeloid leukaemia

Lazzaroni, F., Giacco, L. D., Biasci, D., et al. (2017) · Scientific Reports · doi:10.1038/srep46788 · Cited by 135 · see citing works

Efficient AUC Optimization for Classification

Calders, T., & Jaroszewicz, S. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74976-9_8 · Cited by 123 · see citing works

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

Wang, S., Zeng, Y., Liu, X., Zhu, E., Yin, J., Xu, C., & Kloft, M. (2019) · neural information processing systems · Cited by 89 · see citing works

Satellite image forgery detection and localization using GAN and One-Class classifier

Yarlagadda, S. K., Güera, D., Bestagini, P., Zhu, F. M., Tubaro, S., & Delp, E. J. (2018) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · doi:10.2352/issn.2470-1173.2018.07.mwsf-214 · Cited by 67 · see citing works

Automatic Shortcut Removal for Self-Supervised Representation LearningPreprint

Minderer, M., Bachem, O., Houlsby, N., & Tschannen, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08822 · Cited by 48 · see citing works

Developments in Unsupervised Outlier Detection Research

Wang, X., Wang, X., Wang, X., et al. (2020) · Springer eBooks · doi:10.1007/978-981-15-9519-6_2 · Cited by 7 · see citing works

Towards Total Recall in Industrial Anomaly Detection

Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810 · 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

DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

Zavrtanik, V., Kristan, M., & Skocaj, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00822 · Cited by 1,086 · 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

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01878 · Cited by 585 · 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

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

Ristea, N.-C., Madan, N., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01321 · Cited by 315 · 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

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

FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., & Wu, L. (2021) · arXiv · doi:10.48550/arxiv.2111.07677 · Cited by 210 · see citing works

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

Ding, C., Pang, G., & Shen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00724 · Cited by 197 · 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

Unsupervised Image Anomaly Detection and Segmentation Based on Pretrained Feature Mapping

Wan, Q., Gao, L., Li, X., & Wen, L. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3182385 · Cited by 120 · 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

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

Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. H., Verjans, J. W., Singh, R., & Carneiro, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87240-3_13 · Cited by 77 · see citing works

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

Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., & Wu, L. (2022) · 2022 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859925 · Cited by 80 · see citing works

Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition

Yan, Y., Wang, D., Zhou, G., & Chen, Q. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3107586 · Cited by 60 · see citing works

FAPM: Fast Adaptive Patch Memory for Real-Time Industrial Anomaly Detection

Kim, D., Park, C., Cho, S., & Lee, S. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10096400 · Cited by 56 · 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

A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection

Hu, C., Chen, K., & Shao, H. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428370 · Cited by 24 · see citing works

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 · see citing works

PEDENet: Image anomaly localization via patch embedding and density estimation

Zhang, K., Wang, B., & Kuo, C.-C. J. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.11.030 · Cited by 22 · see citing works

Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

Rippel, O., Chavan, A., Lei, C., & Merhof, D. (2022) · Proceedings of the 2nd International Conference on Image Processing and Vision Engineering · doi:10.5220/0011063900003209 · Cited by 17 · see citing works

A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection

Tailanian, M., Muse, P., & Pardo, A. (2021) · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla52953.2021.00035 · Cited by 10 · see citing works

Towards Experienced Anomaly Detector Through Reinforcement Learning

Huang, C., Wu, Y., Zuo, Y., Pei, K., & Min, G. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12130 · Cited by 59 · see citing works

Tree-based Self-adaptive Anomaly Detection by Human-Machine Interaction

Li, Q., Yu, Z., Xu, H., & Guo, B. (2021) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms53169.2021.9582631 · Cited by 1 · see citing works

Human-machine interactive streaming anomaly detection by online self-adaptive forest

Li, Q., Yu, Z., Huang, X., & Guo, B. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1270-y · Cited by 10 · see citing works

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

Kim, D., Antariksa, G., Handayani, M. P., Lee, S.-B., & Lee, J. (2021) · Sensors · doi:10.3390/s21155200 · Cited by 70 · see citing works

Anomaly Detection with Partially Observed Anomaly Types

Zhang, W., Gao, L., Li, S., et al. (2021) · International Conference on Computer Communication and Network Security (CCNS) · doi:10.1109/ccns53852.2021.00028 · Cited by 4 · see citing works