Fetching the paper…
Reading the bibliography…
The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples.
On optimum recognition error and reject tradeoff
C Chow · 1970
Earlier work this paper cites.
Maximum likelihood estimation: Logic and practice
Scott R Eliason · 1993
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C. Platt, John C. Shawe-Taylor, Alex J. Smola, and Robert C. Williamson · 2001
Earlier work this paper cites.
Support vector data description
David M. J. Tax and Robert P. W. Duin · 2004
Earlier work this paper cites.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
Earlier work this paper cites.
Fast mining of distance-based outliers in high-dimensional datasets
Amol Ghoting, Srinivasan Parthasarathy, and Matthew Eric Otey · 2008
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.
Density estimation by dual ascent of the log-likelihood
Esteban G Tabak, Eric Vanden-Eijnden, et al · 2010
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.
Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Caglar Gulcehre, Universite ’De Montreal, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
Cited alongside, same era.
Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Later among the works it cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Later among the works it cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q Weinberger · 2017
Later among the works it cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Cited alongside, same era.
Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Cited alongside, same era.
Detecting anomalous data using auto-encoders
Jerone Andrews, Edward Morton, and Lewis Griffin · 2016
Cited alongside, same era.
A hybrid autoencoder and density estimation model for anomaly detection
Van Loi Cao, Miguel Nicolau, and James Mcdermott · 2016
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 2016
Cited alongside, same era.
William Wang, Angelina Wang, Aviv Tamar, Xi Chen, and Pieter Abbeel · 2017
Later among the works it cites.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Later among the works it cites.
Principled detection of out-of-distribution examples in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
Later among the works it cites.
Deep one-class classification
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib A. Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Later among the works it cites.
Learning neural random fields with inclusive auxiliary generators
Yunfu Song and Zhijian Ou · 2018
Later among the works it cites.
Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
Later among the works it cites.
Zero-shot out-of-distribution detection with feature correlations, 2020
Chandramouli S Sastry and Sageev Oore · 2020
Closest in time.
Out-of-distribution image detection using the normalized compression distance, 2020
Sehun Yu, Donga Lee, and Hwanjo Yu · 2020
Closest in time.