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We ask the following question: what training information is required to design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting samples that lie far away from the training distribution? Since unlabeled data is easily accessible for many applications, the most compelling approach is to develop detectors based on only unlabeled in-distribution data.
On the generalized distance in statistics
Prasanta Chandra Mahalanobis · 1936
Earlier work this paper cites.
Procedures for detecting outlying observations in samples
Frank E Grubbs · 1969
Earlier work this paper cites.
Estimation of a covariance matrix
C. Stein · 1975
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson · 2001
Earlier work this paper cites.
Outlier detection using replicator neural networks
Simon Hawkins, Hongxing He, Graham Williams, and Rohan Baxter · 2002
Earlier work this paper cites.
Honey, i shrunk the sample covariance matrix
Olivier Ledoit and Michael Wolf · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Toward supervised anomaly detection
Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
Earlier work this paper cites.
Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Earlier work this paper cites.
Toward open-set face recognition
Manuel Günther, Steve Cruz, Ethan M Rudd, and Terrance E Boult · 2017
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Earlier work this paper cites.
Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
Cited alongside, same era.
Detection of anomalies in large scale accounting data using deep autoencoder networks
Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, and Bernd Reimer · 2017
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
Cited alongside, same era.
Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
Later among the works it cites.
Ocgan: One-class novelty detection using gans with constrained latent representations
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang · 2019
Later among the works it cites.
Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
Later among the works it cites.
Deep semi-supervised anomaly detection
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft · 2019
Later among the works it cites.
Detecting out-of-distribution examples with in-distribution examples and gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2019
Later among the works it cites.
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Self-supervised learning for generalizable out-of-distribution detection
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Deep learning for anomaly detection: A review
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