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Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects.
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ImageNet: A large-scale hierarchical image database
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Attention is all you need
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GANomaly: Semi-supervised anomaly detection via adversarial training
S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Deep anomaly detection using geometric transformations
I. Golan and R. El-Yaniv · 2018
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Deep one-class classification
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft · 2018
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Adversarially learned one-class classifier for novelty detection
M. Sabokrou, M. Khalooei, M. Fathy, and E. Adeli · 2018
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Iterative energy-based projection on a normal data manifold for anomaly localization
D. Dehaene, O. Frigo, S. Combrexelle, and P. Eline · 2019
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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
D. Gong, L. Liu, V. Le, B. Saha, M. R. Mansour, S. Venkatesh, and A. v. d. Hengel · 2019
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Decoupled weight decay regularization
L. Ilya and H. Frank · 2019
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OCGAN: One-class novelty detection using GANs with constrained latent representations
P. Perera, R. Nallapati, and B. Xiang · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
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Detecting semantic anomalies
F. Ahmed and A. Courville · 2020
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger · 2020
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End-to-end object detection with transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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Backpropagated gradient representations for anomaly detection
G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib · 2020
Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection
J. Hou, Y. Zhang, Q. Zhong, D. Xie, S. Pu, and H. Zhou · 2021
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CutPaste: Self-supervised learning for anomaly detection and localization
C.-L. Li, K. Sohn, J. Yoon, and T. Pfister · 2021
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Explainable deep one-class classification
P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, M. Kloft, and K.-R. Müller · 2021
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VT-ADL: A vision transformer network for image anomaly detection and localization
P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, and G. L. Foresti · 2021
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Inpainting transformer for anomaly detection
J. Pirnay and K. Chai · 2021
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G2D: generate to detect anomaly
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Towards visually explaining variational autoencoders
W. Liu, R. Li, M. Zheng, S. Karanam, Z. Wu, B. Bhanu, R. J. Radke, and O. Camps · 2020
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Learning memory-guided normality for anomaly detection
H. Park, J. Noh, and B. Ham · 2020
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Synthesize then compare: Detecting failures and anomalies for semantic segmentation
Y. Xia, Y. Zhang, F. Liu, W. Shen, and A. L. Yuille · 2020
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Patch SVDD: Patch-level SVDD for anomaly detection and segmentation
J. Yi and S. Yoon · 2020
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Old is gold: Redefining the adversarially learned one-class classifier training paradigm
M. Z. Zaheer, J.-h. Lee, M. Astrid, and S.-I. Lee · 2020
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Encoding structure-texture relation with P-Net for anomaly detection in retinal images
K. Zhou, Y. Xiao, J. Yang, J. Cheng, W. Liu, W. Luo, Z. Gu, J. Liu, and S. Gao · 2020
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M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, and M. Sabokrou · 2021
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Panda: Adapting pretrained features for anomaly detection and segmentation
T. Reiss, N. Cohen, L. Bergman, and Y. Hoshen · 2021
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Modeling the distribution of normal data in pretrained deep features for anomaly detection
O. Rippel, P. Mertens, and D. Merhof · 2021
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Multiresolution knowledge distillation for anomaly detection
M. Salehi, N. Sadjadi, S. Baselizadeh, M. H. Rohban, and H. R. Rabiee · 2021
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Unsupervised anomaly segmentation via deep feature reconstruction
Y. Shi, J. Yang, and Z. Qi · 2021
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Learning semantic context from normal samples for unsupervised anomaly detection
X. Yan, H. Zhang, X. Xu, X. Hu, and P.-A. Heng · 2021
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DRAEM-A discriminatively trained reconstruction embedding for surface anomaly detection
V. Zavrtanik, M. Kristan, and D. Skočaj · 2021
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UTRAD: Anomaly detection and localization with U-transformer
L. Chen, Z. You, N. Zhang, J. Xi, and X. Le · 2022
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Anomaly detection via reverse distillation from one-class embedding
H. Deng and X. Li · 2022
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ADTR: Anomaly detection transformer with feature reconstruction
Z. You, K. Yang, W. Luo, L. Cui, Y. Zheng, and X. Le · 2022
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L. Yunseung and K. Pilsung · 2022
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