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Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution.
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Robust out-of-distribution detection for neural networks
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 2003
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Convolutional deep belief networks on cifar-10
Alex Krizhevsky and Geoff Hinton · 2009
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Cifar-10 and cifar-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Robust statistics
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Kylberg texture dataset v. 1.0
Gustaf Kylberg · 2011
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Notmnist dataset
Yaroslav Bulatov · 2011
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Robust statistics: the approach based on influence functions
Peter J Rousseeuw, Frank R Hampel, Elvezio M Ronchetti, and Werner A Stahel · 2011
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The MNIST database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander J. Smola · 2012
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Shai Shalev-Shwartz and Shai Ben-David · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Positive-unlabeled learning with non-negative risk estimator
Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, and Masashi Sugiyama · 2017
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Depth-width tradeoffs in approximating natural functions with neural networks
Itay Safran and Ohad Shamir · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Dae-ki Cho, and Haifeng Chen · 2018
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Generative probabilistic novelty detection with adversarial autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, and Gianfranco Doretto · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Deep one-class classification
Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Image anomaly detection with generative adversarial networks
Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
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Places: A 10 million image database for scene recognition
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Open category detection with PAC guarantees
Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, and Dan Hendrycks · 2018
What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation
Jiahua Dong, Yang Cong, Gan Sun, Bineng Zhong, and Xiaowei Xu · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Mood: Multi-level out-of-distribution detection
Ziqian Lin, Sreya Dutta Roy, and Yixuan Li · 2021
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Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2021
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Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance E. Boult · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Binary classification from positive-confidence data
Takashi Ishida, Gang Niu, and Masashi Sugiyama · 2018
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Do deep generative models know what they don’t know?
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas G. Dietterich · 2019
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Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, and Balaji Lakshminarayanan · 2019
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React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
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On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Learning bounds for open-set learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, and Guangquan Zhang · 2021
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Evidential deep learning for open set action recognition
Wentao Bao, Qi Yu, and Yu Kong · 2021
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A simple fix to mahalanobis distance for improving near-ood detection
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 2021
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Understanding failures in out-of-distribution detection with deep generative models
Lily H. Zhang, Mark Goldstein, and Rajesh Ranganath · 2021
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Optimal strategies for reject option classifiers
Vojtech Franc, Daniel Průša, and V. Voracek · 2021
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Classification with rejection based on cost-sensitive classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, and Masashi Sugiyama · 2021
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Outlier-robust learning of ising models under dobrushin’s condition
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, and Yuxin Sun · 2021
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Outlier-robust sparse estimation via non-convex optimization
Yu Cheng, Ilias Diakonikolas, Daniel M Kane, Rong Ge, Shivam Gupta, and Mahdi Soltanolkotabi · 2021
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Efficient learning with arbitrary covariate shift
Adam Tauman Kalai and Varun Kanade · 2021
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Can multi-label classification networks know what they don’t know?
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li · 2021
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Out-of-distribution detection using union of 1-dimensional subspaces
Alireza Zaeemzadeh, Niccoló Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, and Mubarak Shah · 2021
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On the impact of spurious correlation for out-of-distribution detection
Yifei Ming, Hang Yin, and Yixuan Li · 2022
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Opental: Towards open set temporal action localization
Wentao Bao, Qi Yu, and Yu Kong · 2022
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Full-spectrum out-of-distribution detection
Jingkang Yang, Kaiyang Zhou, and Ziwei Liu · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Provable guarantees for understanding out-of-distribution detection
Peyman Morteza and Yixuan Li · 2022
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Outlier-robust sparse mean estimation for heavy-tailed distributions
Ilias Diakonikolas, Daniel M Kane, Jasper CH Lee, and Ankit Pensia · 2022
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Semi-supervised heterogeneous domain adaptation: Theory and algorithms
Zhen Fang, Jie Lu, Feng Liu, and Guangquan Zhang · 2022
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