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Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems.
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Clustering using the fisher-rao distance
João E. Strapasson, Julianna Pinele, and Sueli I. R. Costa · 2016
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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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Densely connected convolutional networks
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Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, 2017
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Places: A 10 million image database for scene recognition
Certifiably adversarially robust detection of out-of-distribution data
Julian Bitterwolf, Alexander Meinke, and Matthias Hein · 2020
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Performance analysis of out-of-distribution detection on trained neural networks
Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, and Cristofer Englund · 2020
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Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Self-supervised learning for generalizable out-of-distribution detection
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Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Generative ensembles for robust anomaly detection
Hyun-Jae Choi and Eric Jang · 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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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Out-of-distribution detection using multiple semantic label representations
Gabi Shalev, Yossi Adi, and Joseph Keshet · 2018
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Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L. Willke · 2018
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Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang · 2020
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The fisher–rao distance between multivariate normal distributions: Special cases, bounds and applications
Julianna Pinele, João E. Strapasson, and Sueli I. R. Costa · 2020
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Detecting out-of-distribution examples with Gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Contrastive training for improved out-of-distribution detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon A. A. Kohl, taylan. cemgil, S. M. Ali Eslami, and Olaf Ronneberger · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit · 2020
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Hybrid models for open set recognition
Hongjie Zhang, Ang Li, Jie Guo, and Yanwen Guo · 2020
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Deep residual flow for out of distribution detection
Ev Zisselman and Aviv Tamar · 2020
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, 2021
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Removing undesirable feature contributions using out-of-distribution data
Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, and Sungroh Yoon · 2021
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Multiscale score matching for out-of-distribution detection
Ahsan Mahmood, Junier Oliva, and Martin Andreas Styner · 2021
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Understanding the failure modes of out-of-distribution generalization
Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur · 2021
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Adversarial robustness via fisher-rao regularization
Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, and Pablo Piantanida · 2021
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A simple fix to mahalanobis distance for improving near-ood detection, 2021
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 2021
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Out-of-distribution detection with distance guarantee in deep generative models, 2021
Yufeng Zhang, Wanwei Liu, Zhenbang Chen, Ji Wang, Zhiming Liu, Kenli Li, and Hongmei Wei · 2021
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