Fetching the paper…
Reading the bibliography…
The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD).
ACM Siggraph Computer Graphics 19
K. Perlin, An image synthesizer · 1985
Earlier work this paper cites.
E. Eskin, A.O. Arnold, M.J. Prerau, L. Portnoy, S. Stolfo, in Applications of Data Mining in Computer Security (2002)
2002
Earlier work this paper cites.
Machine learning 54
D.M. Tax, R.P. Duin, Support vector data description · 2004
Earlier work this paper cites.
M. Wieler, T. Hahn, in DAGM symposium in (2007)
2007
Earlier work this paper cites.
2009 IEEE international conference on robotics and automation pp. 3212–3217 (2009)
R.B. Rusu, N. Blodow, M. Beetz, Fast point feature histograms (fpfh) for 3d registration · 2009
Earlier work this paper cites.
Applied Surface Science 285
K. Song, Y. Yan, A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects · 2013
Earlier work this paper cites.
arXiv preprint arXiv:1409.1556 (2014)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition · 2014
Earlier work this paper cites.
IEEE Transactions on Industrial Informatics 10
X. Bai, Y. Fang, W. Lin, L. Wang, B.F. Ju, Saliency-based defect detection in industrial images by using phase spectrum · 2014
Earlier work this paper cites.
Materials Science and Engineering: A 593
H. Attar, M. Calin, L. Zhang, S. Scudino, J. Eckert, Manufacture by selective laser melting and mechanical behavior of commercially pure titanium · 2014
Earlier work this paper cites.
D.J. Rezende, S. Mohamed, in International Conference on Machine Learning (2015)
2015
Earlier work this paper cites.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 770–778 (2015)
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition · 2015
Earlier work this paper cites.
International Conference on Medical image computing and computer-assisted intervention pp. 234–241 (2015)
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation · 2015
Earlier work this paper cites.
Journal of Nondestructive Evaluation 34
D. Mery, V. Riffo, U. Zscherpel, G. Mondragón, I. Lillo, I. Zuccar, H. Lobel, M. Carrasco, Gdxray: The database of x-ray images for nondestructive testing · 2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, in Proceedings of the IEEE conference on computer vision and pattern recognition (2016), pp. 770–778
2016
Earlier work this paper cites.
Pattern Recognition 51
C.S. Tsang, H.Y. Ngan, G.K. Pang, Fabric inspection based on the elo rating method · 2016
Earlier work this paper cites.
IEEE Transactions on Industrial Informatics 13
D. Carrera, F. Manganini, G. Boracchi, E. Lanzarone, Defect detection in sem images of nanofibrous materials · 2016
Earlier work this paper cites.
M.A. Rahman, Y. Wang, in International symposium on visual computing (Springer, 2016), pp. 234–244
2016
Earlier work this paper cites.
F. Chollet, in Proceedings of the IEEE conference on computer vision and pattern recognition (2017), pp. 1251–1258
2017
Earlier work this paper cites.
R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, in Proceedings of the IEEE international conference on computer vision (2017), pp. 618–626
2017
Earlier work this paper cites.
International Journal of Computer Vision 122
V.A. Sindagi, S. Srivastava, Domain adaptation for automatic oled panel defect detection using adaptive support vector data description · 2017
Earlier work this paper cites.
Proceedings of the IEEE international conference on computer vision pp. 2223–2232 (2017)
J.Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks · 2017
Earlier work this paper cites.
Advances in neural information processing systems 30
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, S. Hochreiter, Gans trained by a two time-scale update rule converge to a local nash equilibrium · 2017
Earlier work this paper cites.
IEEE Sensors Journal 17
J. Gan, Q. Li, J. Wang, H. Yu, A hierarchical extractor-based visual rail surface inspection system · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1807.03748 (2018)
A.v.d. Oord, Y. Li, O. Vinyals, Representation learning with contrastive predictive coding · 2018
Earlier work this paper cites.
Proceedings of the European conference on computer vision (ECCV) pp. 3–19 (2018)
S. Woo, J. Park, J.Y. Lee, I.S. Kweon, Cbam: Convolutional block attention module · 2018
Earlier work this paper cites.
arXiv preprint arXiv:1807.02011 (2018)
P. Bergmann, S. Löwe, M. Fauser, D. Sattlegger, C. Steger, Improving unsupervised defect segmentation by applying structural similarity to autoencoders · 2018
Earlier work this paper cites.
Proceedings of the IEEE conference on computer vision and pattern recognition pp. 7132–7141 (2018)
J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks · 2018
Earlier work this paper cites.
arXiv preprint arXiv:1808.02518 (2018)
M. Ferguson, R. Ak, Y.T.T. Lee, K.H. Law, Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning · 2018
Earlier work this paper cites.
Applied Sciences (2018)
X. Tao, D. Zhang, W. Ma, X. Liu, D. Xu, Automatic metallic surface defect detection and recognition with convolutional neural networks · 2018
Earlier work this paper cites.
IFAC-PapersOnLine 51
J. Li, Z. Su, J. Geng, Y. Yin, Real-time detection of steel strip surface defects based on improved yolo detection network · 2018
Earlier work this paper cites.
International Conference on Learning Representations (2018)
M. Bińkowski, D.J. Sutherland, M. Arbel, A. Gretton, Demystifying mmd gans · 2018
Earlier work this paper cites.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp. 9592–9600 (2019)
P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection · 2019
Earlier work this paper cites.
IEEE transactions on pattern analysis and machine intelligence 42
D. Zhang, J. Han, Y. Zhang, D. Xu, Synthesizing supervision for learning deep saliency network without human annotation · 2019
Earlier work this paper cites.
International Conference on Learning Representations (2019)
D. Dehaene, O. Frigo, S. Combrexelle, P. Eline, Iterative energy-based projection on a normal data manifold for anomaly localization · 2019
Earlier work this paper cites.
Journal of Intelligent Manufacturing 31
D. Tabernik, S. Sela, J. Skvarc, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection · 2019
Earlier work this paper cites.
IEEE Transactions on Image Processing 29
J. Liu, C. Wang, H. Su, B. Du, D. Tao, Multistage gan for fabric defect detection · 2019
Earlier work this paper cites.
Autex Research Journal 19
J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results · 2019
Earlier work this paper cites.
arXiv preprint arXiv:1902.06197 (2019)
S. Tang, F. He, X. Huang, J. Yang, Online pcb defect detector on a new pcb defect dataset · 2019
Earlier work this paper cites.
Severstal: Steel defect detection
Kaggle · 2019
Earlier work this paper cites.
IEEE Transactions on Medical Imaging (2023)
C. Fang, Q. Wang, L. Cheng, Z. Gao, C. Pan, Z. Cao, Z. Zheng, D. Zhang, Reliable mutual distillation for medical image segmentation under imperfect annotations · 2019
Earlier work this paper cites.
Sensors 20
T. Czimmermann, G. Ciuti, M. Milazzo, M. Chiurazzi, S. Roccella, C.M. Oddo, P. Dario, Visual-based defect detection and classification approaches for industrial applications—a survey · 2020
Earlier work this paper cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 4183–4192 (2020)
P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings · 2020
Earlier work this paper cites.
Proceedings of the Asian Conference on Computer Vision (2020)
J. Yi, S. Yoon, Patch svdd: Patch-level svdd for anomaly detection and segmentation · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2002.10445 (2020)
L. Bergman, N. Cohen, Y. Hoshen, Deep nearest neighbor anomaly detection · 2020
Earlier work this paper cites.
International Conference on Learning Representations (2020)
K. Sohn, C.L. Li, J. Yoon, M. Jin, T. Pfister, Learning and evaluating representations for deep one-class classification · 2020
Earlier work this paper cites.
IEEE Transactions on Industrial Informatics 17
M. Niu, K. Song, L. Huang, Q. Wang, Y. Yan, Q. Meng, Unsupervised saliency detection of rail surface defects using stereoscopic images · 2020
Earlier work this paper cites.
IEEE Access 8
Y. Qiu, L. Tang, B. Li, S. Niu, T. Niu, Uneven illumination surface defects inspection based on saliency detection and intrinsic image decomposition · 2020
Earlier work this paper cites.
International Conference on Learning Representations (2020)
P. Liznerski, L. Ruff, R.A. Vandermeulen, B.J. Franks, M. Kloft, K.R. Muller, Explainable deep one-class classification · 2020
Earlier work this paper cites.
European Conference on Computer Vision pp. 485–503 (2020)
S. Venkataramanan, K.C. Peng, R.V. Singh, A. Mahalanobis, Attention guided anomaly localization in images · 2020
Earlier work this paper cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
A.S. Iquebal, S.T. Bukkapatnam, Consistent estimation of the max-flow problem: Towards unsupervised image segmentation · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2010.11929 (2020)
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An image is worth 16x16 words: Transformers for image recognition at scale · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2005.02357 (2020)
N. Cohen, Y. Hoshen, Sub-image anomaly detection with deep pyramid correspondences · 2020
Earlier work this paper cites.
IEEE Access 8
H. Chung, J. Park, J. Keum, H. Ki, S. Kang, Unsupervised anomaly detection using style distillation · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2012.07122 (2020)
J. Yang, Y. Shi, Z. Qi, Dfr: Deep feature reconstruction for unsupervised anomaly segmentation · 2020
Earlier work this paper cites.
European conference on computer vision pp. 360–377 (2020)
K. Zhou, Y. Xiao, J. Yang, J. Cheng, W. Liu, W. Luo, Z. Gu, J. Liu, S. Gao, Encoding structure-texture relation with p-net for anomaly detection in retinal images · 2020
Earlier work this paper cites.
Bmvc (2020)
Z. Li, N. Li, K. Jiang, Z. Ma, X. Wei, X. Hong, Y. Gong, Superpixel masking and inpainting for self-supervised anomaly detection · 2020
Earlier work this paper cites.
H. Nakanishi, M. Suzuki, Y. Matsuo, Iterative image inpainting with structural similarity mask for anomaly detection (2020)
2020
Earlier work this paper cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 8642–8651 (2020)
W. Liu, R. Li, M. Zheng, S. Karanam, Z. Wu, B. Bhanu, R.J. Radke, O. Camps, Towards visually explaining variational autoencoders · 2020
Earlier work this paper cites.
IEEE Transactions on Cybernetics (2020)
T. Matsubara, K. Sato, K. Hama, R. Tachibana, K. Uehara, Deep generative model using unregularized score for anomaly detection with heterogeneous complexity · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2008.05369 (2020)
D. Dehaene, P. Eline, Anomaly localization by modeling perceptual features · 2020
Earlier work this paper cites.
Applied Sciences 10
L. Wang, D. Zhang, J. Guo, Y. Han, Image anomaly detection using normal data only by latent space resampling · 2020
Earlier work this paper cites.
Advances in Neural Information Processing Systems 33
J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models · 2020
Earlier work this paper cites.
European conference on computer vision pp. 751–766 (2020)
W.H. Chu, K.M. Kitani, Neural batch sampling with reinforcement learning for semi-supervised anomaly detection · 2020
Cited alongside, same era.
Optics and Lasers in Engineering 128
G. Song, K. Song, Y. Yan, Saliency detection for strip steel surface defects using multiple constraints and improved texture features · 2020
Cited alongside, same era.
IEEE Transactions on Image Processing 30
B. Hu, B. Gao, W.L. Woo, L. Ruan, J. Jin, Y. Yang, Y. Yu, A lightweight spatial and temporal multi-feature fusion network for defect detection · 2020
Cited alongside, same era.
IEEE Transactions on Pattern Analysis and Machine Intelligence 44
D. Zhang, W. Zeng, J. Yao, J. Han, Weakly supervised object detection using proposal-and semantic-level relationships · 2020
Cited alongside, same era.
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 1
O. Rippel, M. Müller, D. Merhof, Gan-based defect synthesis for anomaly detection in fabrics · 2020
Cited alongside, same era.
Proceedings of the IEEE/CVF International Conference on Computer Vision pp. 8495–8504 (2021)
S. Sheynin, S. Benaim, L. Wolf, A hierarchical transformation-discriminating generative model for few shot anomaly detection · 2021
Later among the works it cites.
Pattern Recognition 110
D. Zhang, G. Huang, Q. Zhang, J. Han, J. Han, Y. Yu, Cross-modality deep feature learning for brain tumor segmentation · 2021
Later among the works it cites.
IEEE Transactions on Instrumentation and Measurement (2022)
X. Tao, X. Gong, X. Zhang, S. Yan, C. Adak, Deep learning for unsupervised anomaly localization in industrial images: A survey · 2022
Later among the works it cites.
arXiv preprint arXiv:2204.11161 (2022)
Y. Cui, Z. Liu, S. Lian, A survey on unsupervised industrial anomaly detection algorithms · 2022
Later among the works it cites.
arXiv preprint arXiv:2206.03687 (2022)
Z. You, L. Cui, Y. Shen, K. Yang, X. Lu, Y. Zheng, X. Le, A unified model for multi-class anomaly detection · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
IEEE Transactions on Automation Science and Engineering 17
S. Niu, B. Li, X. Wang, H. Lin, Defect image sample generation with gan for improving defect recognition · 2020
Cited alongside, same era.
2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems 11439
T. Wei, D. Cao, X. Jiang, C. Zheng, L. Liu, Defective samples simulation through neural style transfer for automatic surface defect segment · 2020
Cited alongside, same era.
Neurocomputing 412
T. Wei, D. Cao, C. Zheng, Q. Yang, A simulation-based few samples learning method for surface defect segmentation · 2020
Cited alongside, same era.
Journal of Intelligent Manufacturing pp. 1–14 (2020)
S. Jain, G. Seth, A. Paruthi, U. Soni, G. Kumar, Synthetic data augmentation for surface defect detection and classification using deep learning · 2020
Cited alongside, same era.
Journal of Intelligent Manufacturing 31
D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection · 2020
Cited alongside, same era.
The Visual Computer 36
Y. Huang, C. Qiu, K. Yuan, Surface defect saliency of magnetic tile · 2020
Cited alongside, same era.
IEEE Transactions on Image Processing 29
D. Zhang, G. Huang, Q. Zhang, J. Han, J. Han, Y. Wang, Y. Yu, Exploring task structure for brain tumor segmentation from multi-modality mr images · 2020
Cited alongside, same era.
Later among the works it cites.
International Journal of Computer Vision 130
P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, C. Steger, Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 9737–9746 (2022)
H. Deng, X. Li, Anomaly detection via reverse distillation from one-class embedding · 2022
Later among the works it cites.
Knowledge-Based Systems 248
Y. Cao, Q. Wan, W. Shen, L. Gao, Informative knowledge distillation for image anomaly segmentation · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.07829 (2022)
M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Asymmetric student-teacher networks for industrial anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.07548 (2022)
S. Yamada, S. Kamiya, K. Hotta, Reconstructed student-teacher and discriminative networks for anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2205.00908 (2022)
M. Yang, P. Wu, J. Liu, H. Feng, Memseg: A semi-supervised method for image surface defect detection using differences and commonalities · 2022
Later among the works it cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
M. Zhuge, D.P. Fan, N. Liu, D. Zhang, D. Xu, L. Shao, Salient object detection via integrity learning · 2022
Later among the works it cites.
Science China Information Sciences 65
C. Fang, H. Tian, D. Zhang, Q. Zhang, J. Han, J. Han, Densely nested top-down flows for salient object detection · 2022
Later among the works it cites.
Pattern Recognition Letters 153
K. Zhang, B. Wang, C.C.J. Kuo, Pedenet: Image anomaly localization via patch embedding and density estimation · 2022
Later among the works it cites.
IEEE Transactions on Industrial Informatics (2022)
Q. Wan, L. Gao, X. Li, L. Wen, Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping · 2022
Later among the works it cites.
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) pp. 876–881 (2022)
Q. Wan, Y. Cao, L. Gao, W. Shen, X. Li, Position encoding enhanced feature mapping for image anomaly detection · 2022
Later among the works it cites.
2022 IEEE International Conference on Multimedia and Expo (ICME) pp. 1–6 (2022)
Y. Zheng, X. Wang, R. Deng, T. Bao, R. Zhao, L. Wu, Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision pp. 1088–1097 (2022)
M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision pp. 98–107 (2022)
D. Gudovskiy, S. Ishizaka, K. Kozuka, Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows · 2022
Later among the works it cites.
arXiv preprint arXiv:2206.01992 (2022)
R. Yan, F. Zhang, M. Huang, W. Liu, D. Hu, J. Li, Q. Liu, J. Jiang, Q. Guo, L. Zheng, Cainnflow: Convolutional block attention modules and invertible neural networks flow for anomaly detection and localization tasks · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.14913 (2022)
Y. Kim, H. Jang, D. Lee, H.J. Choi, Altub: Alternating training method to update base distribution of normalizing flow for anomaly detection · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision pp. 3992–4000 (2022)
C.C. Tsai, T.H. Wu, S.H. Lai, Multi-scale patch-based representation learning for image anomaly detection and segmentation · 2022
Later among the works it cites.
European Conference on Computer Vision pp. 392–408 (2022)
Y. Zou, J. Jeong, L. Pemula, D. Zhang, O. Dabeer, Spot-the-difference self-supervised pre-training for anomaly detection and segmentation · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 14,318–14,328 (2022)
K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection · 2022
Later among the works it cites.
IEEE Access 10
S. Lee, S. Lee, B.C. Song, Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization · 2022
Later among the works it cites.
arXiv preprint arXiv:2211.07381 (2022)
D. Kim, C. Park, S. Cho, S. Lee, Fapm: Fast adaptive patch memory for real-time industrial anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2211.12634 (2022)
J. Bae, J.H. Lee, S. Kim, Image anomaly detection and localization with position and neighborhood information · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.08768 (2022)
J. Jang, E. Hwang, S.H. Park, N-pad: Neighboring pixel-based industrial anomaly detection · 2022
Later among the works it cites.
IEEE Transactions on Industrial Informatics 18
X. Tao, D. Zhang, W. Ma, Z. Hou, Z. Lu, C. Adak, Unsupervised anomaly detection for surface defects with dual-siamese network · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.14485 (2022)
T. Liu, B. Li, Z. Zhao, X. Du, B. Jiang, L. Geng, Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection · 2022
Later among the works it cites.
IEEE Access 10
D. Kim, D. Jeong, H. Kim, K. Chong, S. Kim, H. Cho, Spatial contrastive learning for anomaly detection and localization · 2022
Later among the works it cites.
arXiv preprint arXiv:2206.11723 (2022)
A. Bauer, Self-supervised training with autoencoders for visual anomaly detection · 2022
Later among the works it cites.
IEEE Transactions on Multimedia (2022)
C. Huang, Q. Xu, Y. Wang, Y. Wang, Y. Zhang, Self-supervised masking for unsupervised anomaly detection and localization · 2022
Later among the works it cites.
arXiv preprint arXiv:2209.12440 (2022)
P. Xing, Y. Sun, Z. Li, Self-supervised guided segmentation framework for unsupervised anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2208.01521 (2022)
V. Zavrtanik, M. Kristan, D. Skočaj, Dsr–a dual subspace re-projection network for surface anomaly detection · 2022
Later among the works it cites.
European Conference on Computer Vision pp. 474–489 (2022)
H.M. Schlüter, J. Tan, B. Hou, B. Kainz, Natural synthetic anomalies for self-supervised anomaly detection and localization · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 13,576–13,586 (2022)
N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2209.12148 (2022)
N. Madan, N.C. Ristea, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised masked convolutional transformer block for anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2203.00259 (2022)
Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2209.01816 (2022)
Z. You, K. Yang, W. Luo, L. Cui, Y. Zheng, X. Le, Adtr: Anomaly detection transformer with feature reconstruction · 2022
Later among the works it cites.
IEEE Access 10
Y. Lee, P. Kang, Anovit: Unsupervised anomaly detection and localization with vision transformer-based encoder-decoder · 2022
Later among the works it cites.
arXiv preprint arXiv:2208.03486 (2022)
E. Mathian, H. Liu, L. Fernandez-Cuesta, D. Samaras, M. Foll, L. Chen, Haloae: An halonet based local transformer auto-encoder for anomaly detection and localization · 2022
Later among the works it cites.
International Conference on Image Analysis and Processing pp. 394–406 (2022)
J. Pirnay, K. Chai, Inpainting transformer for anomaly detection · 2022
Later among the works it cites.
IEEE Transactions on Industrial Informatics (2022)
J. Jiang, J. Zhu, M. Bilal, Y. Cui, N. Kumar, R. Dou, F. Su, X. Xu, Masked swin transformer unet for industrial anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.15540 (2022)
A. De Nardin, P. Mishra, G.L. Foresti, C. Piciarelli, Masked transformer for image anomaly localization · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 650–656 (2022)
J. Wyatt, A. Leach, S.M. Schmon, C.G. Willcocks, Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise · 2022
Later among the works it cites.
arXiv preprint arXiv:2211.16092 (2022)
Y. Teng, H. Li, F. Cai, M. Shao, S. Xia, Unsupervised visual defect detection with score-based generative model · 2022
Later among the works it cites.
IEEE Transactions on Instrumentation and Measurement 71
Q. Wan, L. Gao, X. Li, Logit inducing with abnormality capturing for semi-supervised image anomaly detection · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7388–7398 (2022)
C. Ding, G. Pang, C. Shen, Catching both gray and black swans: Open-set supervised anomaly detection · 2022
Later among the works it cites.
IEEE Transactions on Neural Networks and Learning Systems (2022)
D. Zhang, G. Guo, W. Zeng, L. Li, J. Han, Generalized weakly supervised object localization · 2022
Later among the works it cites.
European Conference on Computer Vision pp. 303–319 (2022)
C. Huang, H. Guan, A. Jiang, Y. Zhang, M. Spratling, Y.F. Wang, Registration based few-shot anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2211.14307 (2022)
E. Schwartz, A. Arbelle, L. Karlinsky, S. Harary, F. Scheidegger, S. Doveh, R. Giryes, Maeday: Mae for few and zero shot anomaly-detection · 2022
Later among the works it cites.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 16,000–16,009 (2022)
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, R. Girshick, Masked autoencoders are scalable vision learners · 2022
Later among the works it cites.
arXiv preprint arXiv:2202.08088 (2022)
C. Qiu, A. Li, M. Kloft, M. Rudolph, S. Mandt, Latent outlier exposure for anomaly detection with contaminated data · 2022
Later among the works it cites.
Y. Chen, Y. Tian, G. Pang, G. Carneiro, in AAAI (2022)
2022
Later among the works it cites.
arXiv preprint arXiv:2202.11660 (2022)
P. Bergmann, D. Sattlegger, Anomaly detection in 3d point clouds using deep geometric descriptors · 2022
Later among the works it cites.
arXiv preprint arXiv:2203.05550 (2022)
E. Horwitz, Y. Hoshen, Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2210.10773 (2022)
T. Reiss, N. Cohen, E. Horwitz, R. Abutbul, Y. Hoshen, Anomaly detection requires better representations · 2022
Later among the works it cites.
Proceedings of the Asian Conference on Computer Vision pp. 3586–3602 (2022)
L. Bonfiglioli, M. Toschi, D. Silvestri, N. Fioraio, D. De Gregorio, The eyecandies dataset for unsupervised multimodal anomaly detection and localization · 2022
Later among the works it cites.
arXiv preprint arXiv:2211.13968 (2022)
T. Bao, J. Chen, W. Li, X. Wang, J. Fei, L. Wu, R. Zhao, Y. Zheng, Miad: A maintenance inspection dataset for unsupervised anomaly detection · 2022
Later among the works it cites.
arXiv preprint arXiv:2303.14814 (2023)
J. Jeong, Y. Zou, T. Kim, D. Zhang, A. Ravichandran, O. Dabeer, Winclip: Zero-/few-shot anomaly classification and segmentation · 2023
Closest in time.
arXiv preprint arXiv:2301.13359 (2023)
G. Xie, J. Wang, J. Liu, J. Lyu, Y. Liu, C. Wang, F. Zheng, Y. Jin, Im-iad: Industrial image anomaly detection benchmark in manufacturing · 2023
Closest in time.