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Nearest neighbors is a successful and long-standing technique for anomaly detection.
A branch and bound algorithm for computing k-nearest neighbors
Fukunaga, K. and Narendra, P. M · 1975
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Algorithm as 136: A k-means clustering algorithm
Hartigan, J. A. and Wong, M. A · 1979
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Support vector method for novelty detection
Scholkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J., and Platt, J. C · 2000
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A geometric framework for unsupervised anomaly detection
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., and Stolfo, S · 2002
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Support vector data description
Tax, D. M. and Duin, R. P · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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Asirra: a captcha that exploits interest-aligned manual image categorization
Elson, J., Douceur, J. R., Howell, J., and Saul, J · 2007
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Outlier detection with kernel density functions
Latecki, L. J., Lazarevic, A., and Pokrajac, D · 2007
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Robust principal component analysis?
Candès, E. J., Li, X., Ma, Y., and Wright, J · 2011
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Principal component analysis
Jolliffe, I · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Cited alongside, same era.
Learning representations for automatic colorization
Larsson, G., Maire, M., and Shakhnarovich, G · 2016
Cited alongside, same era.
Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., and LeCun, Y · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
Cited alongside, same era.
Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
Cited alongside, same era.
Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Yang, B., Fu, X., Sidiropoulos, N. D., and Hong, M · 2017
Cited alongside, same era.
Deep one-class classification
Ruff, L., Gornitz, N., Deecke, L., Siddiqui, S. A., Vandermeulen, R., Binder, A., Müller, E., and Kloft, M · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Fast and robust segmentation of white blood cell images by self-supervised learning
Zheng, X., Wang, Y., Wang, G., and Liu, J · 2018
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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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Ahmed, F. and Courville, A · 2019
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Group anomaly detection via graph autoencoders
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Deep anomaly detection using geometric transformations
Golan, I. and El-Yaniv, R · 2018
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T. G · 2018
Cited alongside, same era.
D’Oro, P., Nasca, E., Masci, J., and Matteucci, M · 2019
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Statistical analysis of nearest neighbor methods for anomaly detection
Gu, X., Akoglu, L., and Rinaldo, A · 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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Classification-based anomaly detection for general data
Bergman, l. and Hoshen, Y · 2020
Closest in time.
Object detection in optical remote sensing images: A survey and a new benchmark
Li, K., Wan, G., Cheng, G., Meng, L., and Han, J · 2020
Closest in time.