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Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning.
Detecting out-of-distribution inputs to deep generative models using a test for typicality
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Tilda-ein referenzdatensatz zur evaluierung von sichtprüfungsverfahren für textiloberflächen
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Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt · 2000
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Anomaly detection of web-based attacks
C. Kruegel and G. Vigna · 2003
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A survey of outlier detection methodologies
V. Hodge and J. Austin · 2004
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A visual vocabulary for flower classification
M.-E. Nilsback and A. Zisserman · 2006
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Caltech-256 object category dataset, 2007
G. Griffin, A. Holub, and P. Perona · 2007
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Smart factory-a step towards the next generation of manufacturing
D. Lucke, C. Constantinescu, and E. Westkämper · 2008
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Learning multiple layers of features from tiny images, 2009
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A black swan in the money market
J. B. Taylor and J. C. Williams · 2009
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A comprehensive survey of data mining-based fraud detection research
C. Phua, V. Lee, K. Smith, and R. Gayler · 2010
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Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei · 2011
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. Jawahar · 2012
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Food-101–mining discriminative components with random forests
L. Bossard, M. Guillaumin, and L. Van Gool · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Obtaining well calibrated probabilities using bayesian binning
M. P. Naeini, G. Cooper, and M. Hauskrecht · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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Deep structured energy based models for anomaly detection
S. Zhai, Y. Cheng, W. Lu, and Z. Zhang · 2016
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen · 2018
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Implicit generation and modeling with energy based models
Y. Du and I. Mordatch · 2019
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Revisiting self-supervised visual representation learning
A. Kolesnikov, X. Zhai, and L. Beyer · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
P. Perera, R. Nallapati, and B. Xiang · 2019
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Likelihood ratios for out-of-distribution detection
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. Depristo, J. Dillon, and B. Lakshminarayanan · 2019
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Severstal: Steel defect detection, 2019
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P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Confident multiple choice learning
K. Lee, C. Hwang, K. S. Park, and J. Shin · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs · 2017
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Large batch training of convolutional networks
Y. You, I. Gitman, and B. Ginsburg · 2017
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
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Severstal · 2019
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Classification-based anomaly detection for general data
L. Bergman and Y. Hoshen · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Novelty detection via blurring
S. Choi and S.-Y. Chung · 2020
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A framework for contrastive self-supervised learning and designing a new approach
W. Falcon and K. Cho · 2020
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Your classifier is secretly an energy based model and you should treat it like one
W. Grathwohl, K.-C. Wang, J.-H. Jacobsen, D. Duvenaud, M. Norouzi, and K. Swersky · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Supervised contrastive learning
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Adversarial self-supervised contrastive learning
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Self-supervised label augmentation via input transformations
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Hybrid discriminative-generative training via contrastive learning
H. Liu and P. Abbeel · 2020
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Deep semi-supervised anomaly detection
L. Ruff, R. A. Vandermeulen, N. Görnitz, A. Binder, E. Müller, K.-R. Müller, and M. Kloft · 2020
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Input complexity and out-of-distribution detection with likelihood-based generative models
J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, and J. Luque · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
A. Srinivas, M. Laskin, and P. Abbeel · 2020
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What makes for good views for contrastive learning
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
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Contrastive training for improved out-of-distribution detection
J. Winkens, R. Bunel, A. G. Roy, R. Stanforth, V. Natarajan, J. R. Ledsam, P. MacWilliams, P. Kohli, A. Karthikesalingam, and S. Kohl · 2020
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What should not be contrastive in contrastive learning
T. Xiao, X. Wang, A. A. Efros, and T. Darrell · 2020
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Regularizing class-wise predictions via self-knowledge distillation
S. Yun, J. Park, K. Lee, and J. Shin · 2020
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