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Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data.
Prototypical contrastive learning of unsupervised representations
Li, J., Zhou, P., Xiong, C., and Hoi, S. C. H · 2005
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G · 2017
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Improving unsupervised defect segmentation by applying structural similarity to autoencoders
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., and Steger, C · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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Mvtec ad – a comprehensive real-world dataset for unsupervised anomaly detection
Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Trischler, A., and Bengio, Y · 2019
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Putting an end to end-to-end: Gradient-isolated learning of representations
Löwe, S., O’Connor, P., and Veeling, B · 2019
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Padim: a patch distribution modeling framework for anomaly detection and localization
Defard, T., Setkov, A., Loesch, A., and Audigier, R · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Deep semi-supervised anomaly detection
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., and Kloft, M · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., and Shin, J · 2020
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Contrastive training for improved out-of-distribution detection
Winkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., Cemgil, T., Eslami, S. M. A., and Ronneberger, O · 2020
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Driver anomaly detection: A dataset and contrastive learning approach
Kopuklu, O., Zheng, J., Xu, H., and Rigoll, G · 2021
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Explainable deep one-class classification
Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., and Müller, K.-R · 2021
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J., Srinivas, A., Fauw, J. D., Razavi, A., Doersch, C., Eslami, S. M. A., and van den Oord, A · 2020
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The clever hans effect in anomaly detection
Kauffmann, J., Ruff, L., Montavon, G., and Müller, K.-R · 2020
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Towards visually explaining variational autoencoders
Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R. J., and Camps, O · 2020
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Encoding structure-texture relation with p-net for anomaly detection in retinal images
Luo, W., Gu, Z., Liu, J., and Gao, S · 2020
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Cutpaste: Self-supervised learning for anomaly detection and localization
Li, C.-L., Sohn, K., Yoon, J., and Pfister, T
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Neural transformation learning for deep anomaly detection beyond images
Qiu, C., Pfrommer, T., Kloft, M., Mandt, S., and Rudolph, M · 2021
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A unifying review of deep and shallow anomaly detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., and Müller, K.-R · 2021
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Learning and evaluating representations for deep one-class classification
Sohn, K., Li, C.-L., Yoon, J., Jin, M., and Pfister, T · 2021
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Anomaly detection with robust deep autoencoders
Zhou, C. and Paffenroth, R. C · 2021
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