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Fine-grained anomaly detection has recently been dominated by segmentation based approaches.
1.1 über die bestimmung von funktionen durch ihre integralwerte längs gewisser mannigfaltigkeiten
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A geometric framework for unsupervised anomaly detection
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Tax, D. M. and Duin, R. P · 2004
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Robust regression and outlier detection , volume 589
Rousseeuw, P. J. and Leroy, A. M · 2005
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Outlier detection with kernel density functions
Latecki, L. J., Lazarevic, A., and Pokrajac, D · 2007
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Fast locality-sensitive hashing
Dasgupta, A., Kumar, R., and Sarlós, T · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
Goldstein, M. and Dengel, A · 2012
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Ensemble gaussian mixture models for probability density estimation
Glodek, M., Schels, M., and Schwenker, F · 2013
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Sakurada, M. and Yairi, T · 2014
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Sliced and radon wasserstein barycenters of measures
Bonneel, N., Rabin, J., Peyré, G., and Pfister, H · 2015
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The radon cumulative distribution transform and its application to image classification
Kolouri, S., Park, S. R., and Rohde, G. K · 2015
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Collective anomaly detection based on long short-term memory recurrent neural networks
Bontemps, L., Cao, V. L., McDermott, J., and Le-Khac, N.-A · 2016
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Defect detection in sem images of nanofibrous materials
Carrera, D., Manganini, F., Boracchi, G., and Lanzarone, E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Learning representations for automatic colorization
Larsson, G., Maire, M., and Shakhnarovich, G · 2016
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Lstm-based encoder-decoder for multi-sensor anomaly detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., and Shroff, G · 2016
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Loda: Lightweight on-line detector of anomalies
Pevnỳ, T · 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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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Deep anomaly detection using geometric transformations
Golan, I. and El-Yaniv, R · 2018
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Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., and Steger, C · 2021
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A review on outlier/anomaly detection in time series data
Blázquez-García, A., Conde, A., Mori, U., and Lozano, J. A · 2021
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Transfer-based semantic anomaly detection
Deecke, L., Ruff, L., Vandermeulen, R. A., and Bilen, H · 2021
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Padim: a patch distribution modeling framework for anomaly detection and localization
Defard, T., Setkov, A., Loesch, A., and Audigier, R · 2021
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Minirocket: A very fast (almost) deterministic transform for time series classification
Dempster, A., Schmidt, D. F., and Webb, G. I · 2021
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 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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Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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Billion-scale similarity search with GPUs
Johnson, J., Douze, M., and Jégou, H · 2019
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Learning deep features for one-class classification
Perera, P. and Patel, V. M · 2019
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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 · 2019
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Jezek, S., Jonak, M., Burget, R., Dvorak, P., and Skotak, M · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Li, C.-L., Sohn, K., Yoon, J., and Pfister, T · 2021
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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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Mean-shifted contrastive loss for anomaly detection
Reiss, T. and Hoshen, Y · 2021
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Panda: Adapting pretrained features for anomaly detection and segmentation
Reiss, T., Cohen, N., Bergman, L., and Hoshen, Y · 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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Multiresolution knowledge distillation for anomaly detection
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., and Rabiee, H. R · 2021
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., and Steger, C · 2022
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Transformaly-two (feature spaces) are better than one
Cohen, M. J. and Avidan, S · 2022
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An empirical investigation of 3d anomaly detection and segmentation
Horwitz, E. and Hoshen, Y · 2022
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Anomaly detection requires better representations
Reiss, T., Cohen, N., Horwitz, E., Abutbul, R., and Hoshen, Y · 2022
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Towards total recall in industrial anomaly detection
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., and Gehler, P · 2022
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No shifted augmentations (nsa): compact distributions for robust self-supervised anomaly detection
Yousef, M., Ackermann, M., Kurup, U., and Bishop, T · 2023
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