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In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples.
Mixture density networks
Bishop, C. M · 1994
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Fisher discriminant analysis with kernels
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.-R · 1999
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Lof: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
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Estimating the support of a high-dimensional distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C · 2001
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Support vector data description
Tax, D. M. and Duin, R. P · 2004
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Sparse reconstruction cost for abnormal event detection
Cong, Y., Yuan, J., and Liu, J · 2011
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Isolation-based anomaly detection
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Anomaly detection and localization in crowded scenes
Li, W., Mahadevan, V., and Vasconcelos, N · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Predicting multiple structured visual interpretations
Dey, D., Ramakrishna, V., Hebert, M., and Andrew Bagnell, J · 2015
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Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2015
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Confident multiple choice learning
Lee, K., Hwang, C., Park, K., and Shin, J · 2017
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Abnormal event detection in videos using generative adversarial nets
Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., and Sebe, N · 2017
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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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Accurate and diverse sampling of sequences based on a “best of many” sample objective
Bhattacharyya, A., Schiele, B., and Fritz, M · 2018
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Anomaly detection with generative adversarial networks
Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., and Kloft, M · 2018
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Radford, A., Metz, L., and Chintala, S · 2015
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A. and Brox, T · 2016
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Stochastic multiple choice learning for training diverse deep ensembles
Lee, S., Prakash, S. P. S., Cogswell, M., Ranjan, V., Crandall, D., and Batra, D · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Photographic image synthesis with cascaded refinement networks
Chen, Q. and Koltun, V · 2017
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Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., and Hager, G. D
Cited in the paper.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., and Hager, G. D
Cited in the paper.
Ilg, E., Çiçek, Ö., Galesso, S., Klein, A., Makansi, O., Hutter, F., and Brox, T · 2018
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Multisource fusion for robust road detection using online estimated reliabilities
Nguyen, T. T., Spehr, J., Zug, S., and Kruse, 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
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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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