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Mitigating the risk arising from extreme events is a fundamental goal with many applications, such as the modelling of natural disasters, financial crashes, epidemics, and many others.
Bayesian Anomaly Detection Using Extreme Value Theory
Guggilam, S.; Zaidi, S. M. A.; Chandola, V.; and Patra, A. K. 2019 · 1905
Earlier work this paper cites.
Residual Life Time at Great Age
Balkema, A. A.; and De Haan, L. 1974 · 1974
Earlier work this paper cites.
Statistical Inference Using Extreme Order Statistics
Pickands, J. 1975 · 1975
Earlier work this paper cites.
Modelling multivariate extreme value distributions
Tawn, J. A. 1990 · 1990
Earlier work this paper cites.
Computing maximum likelihood estimates for the generalized Pareto distribution
Grimshaw, S. D. 1993 · 1993
Earlier work this paper cites.
An Introduction to Statistical Modeling of Extreme Values
Coles, S.; Bawa, J.; Trenner, L.; and Dorazio, P. 2001 · 2001
Earlier work this paper cites.
Functional Peaks-over-threshold Analysis
de Fondeville, R.; and Davison, A. C. 2020 · 2002
Earlier work this paper cites.
Multivariate generalized Pareto distributions
Rootzén, H.; Tajvidi, N.; et al. 2006 · 2006
Earlier work this paper cites.
Towards Good Practices for Data Augmentation in GAN Training
Tran, N.-T.; Tran, V.-H.; Nguyen, N.-B.; Nguyen, T.-K.; and Cheung, N. 2020 · 2006
Earlier work this paper cites.
Statistics of extremes
Gumbel, E. J. 2012 · 2012
Earlier work this paper cites.
The generalized Pareto process; with a view towards application and simulation
Ferreira, A.; De Haan, L.; et al. 2014 · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
Mirza, M.; and Osindero, S. 2014 · 2014
Earlier work this paper cites.
Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization
Sabourin, A.; and Naveau, P. 2014 · 2014
Earlier work this paper cites.
Extremes on river networks
Asadi, P.; Davison, A. C.; and Engelke, S. 2015 · 2015
Earlier work this paper cites.
Dimension reduction in multivariate extreme value analysis
Chautru, E. 2015 · 2015
Earlier work this paper cites.
Functional regular variations, Pareto processes and peaks over threshold
Dombry, C.; and Ribatet, M. 2015 · 2015
Earlier work this paper cites.
Estimation of hüsler–reiss distributions and brown–resnick processes
Engelke, S.; Malinowski, A.; Kabluchko, Z.; and Schlather, M. 2015 · 2015
Earlier work this paper cites.
Conditional generative adversarial nets for convolutional face generation
Gauthier, J. 2015 · 2015
Earlier work this paper cites.
Efficient inference and simulation for elliptical Pareto processes
Thibaud, E.; and Opitz, T. 2015 · 2015
Earlier work this paper cites.
High-dimensional peaks-over-threshold inference
de Fondeville, R.; and Davison, A. C. 2016 · 2016
Earlier work this paper cites.
Sparse representation of multivariate extremes with applications to anomaly ranking
Goix, N.; Sabourin, A.; and Clémençon, S. 2016 · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2016 · 2016
Cited alongside, same era.
Improved Techniques for Training GANs
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Cited alongside, same era.
Instance Normalization: The Missing Ingredient for Fast Stylization
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2016 · 2016
Cited alongside, same era.
Data Augmentation Generative Adversarial Networks
Antoniou, A.; Storkey, A.; and Edwards, H. 2017 · 2017
Cited alongside, same era.
Wasserstein Generative Adversarial Networks
Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
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Biomedical Data Augmentation Using Generative Adversarial Neural Networks
On Binary Classification in Extreme Regions
Jalalzai, H.; Clémençon, S.; and Sabourin, A. 2018 · 2018
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DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Lim, S. K.; Loo, Y.; Tran, N.-T.; Cheung, N.-M.; Roig, G.; and Elovici, Y. 2018 · 2018
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EEG data augmentation for emotion recognition using a conditional wasserstein GAN
Luo, Y.; and Lu, B.-L. 2018 · 2018
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Ma, X.; Li, B.; Wang, Y.; Erfani, S. M.; Wijewickrema, S.; Schoenebeck, G.; Song, D.; Houle, M. E.; and Bailey, J. 2018 · 2018
Later among the works it cites.
BAGAN: Data Augmentation with Balancing GAN
Mariani, G.; Scheidegger, F.; Istrate, R.; Bekas, C.; and Malossi, C. 2018 · 2018
Later among the works it cites.
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Calimeri, F.; Marzullo, A.; Stamile, C.; and Terracina, G. 2017 · 2017
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
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Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T.; Cha, M.; Kim, H.; Lee, J. K.; and Kim, J. 2017 · 2017
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Conditional image synthesis with auxiliary classifier gans
Odena, A.; Olah, C.; and Shlens, J. 2017 · 2017
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The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L.; and Wang, J. 2017 · 2017
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Anomaly detection in streams with extreme value theory
Siffer, A.; Fouque, P.-A.; Termier, A.; Largouet, C.; and Largouët, C. 2017 · 2017
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Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere
Thomas, A.; Clémençon, S.; Gramfort, A.; and Sabourin, A. 2017 · 2017
Cited alongside, same era.
Ramponi, G.; Protopapas, P.; Brambilla, M.; and Janssen, R. 2018 · 2018
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How good is my GAN?
Shmelkov, K.; Schmid, C.; and Alahari, K. 2018 · 2018
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RenderGAN: Generating Realistic Labeled Data
Sixt, L.; Wild, B.; and Landgraf, T. 2018 · 2018
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Weng, T.-W.; Zhang, H.; Chen, P.-Y.; Yi, J.; Su, D.; Gao, Y.; Hsieh, C.-J.; and Daniel, L. 2018 · 2018
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Emotion classification with data augmentation using generative adversarial networks
Zhu, X.; Liu, Y.; Li, J.; Wan, T.; and Qin, Z. 2018 · 2018
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Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation
Chang, C.-T.; Chuang, S.-P.; and yi Lee, H. 2019 · 2019
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Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation
Choi, J.; Kim, T.-K.; and Kim, C. 2019 · 2019
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Learning more with less: Conditional PGGAN-based data augmentation for brain metastases detection using highly-rough annotation on MR images
Han, C.; Murao, K.; Noguchi, T.; Kawata, Y.; Uchiyama, F.; Rundo, L.; Nakayama, H.; and Satoh, S. 2019 · 2019
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A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
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Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
Siarohin, A.; Lathuilière, S.; Sangineto, E.; and Sebe, N. 2019 · 2019
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DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Zhang, X.; Wang, Z.; Liu, D.; and Ling, Q. 2019 · 2019
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BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series
Zhou, B.; Liu, S.; Hooi, B.; Cheng, X.; and Ye, J. 2019 · 2019
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TellTail: Fast Scoring and Detection of Dense Subgraphs
Hooi, B.; Shin, K.; Lamba, H.; and Faloutsos, C. 2020 · 2020
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Training generative adversarial networks with limited data
Karras, T.; Aittala, M.; Hellsten, J.; Laine, S.; Lehtinen, J.; and Aila, T. 2020 · 2020
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Extreme value theory for anomaly detection – the GPD classifier
Vignotto, E.; and Engelke, S. 2020 · 2020
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Effective Data Augmentation with Multi-Domain Learning GANs
Yamaguchi, S.; Kanai, S.; and Eda, T. 2020 · 2020
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