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Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.
“Describing textures in the wild,”
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi, · 2014
Earlier work this paper cites.
“A baseline for detecting misclassified and out-of-distribution examples in neural networks,”
Dan Hendrycks and Kevin Gimpel, · 2016
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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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“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
Earlier work this paper cites.
“The inaturalist species classification and detection dataset,”
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie, · 2018
Earlier work this paper cites.
“Benchmarking neural network robustness to common corruptions and perturbations,”
Dan Hendrycks and Thomas Dietterich, · 2018
Earlier work this paper cites.
“Bag of tricks for image classification with convolutional neural networks,”
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li, · 2019
Earlier work this paper cites.
“Energy-based out-of-distribution detection,”
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li, · 2020
Earlier work this paper cites.
“Out-of-distribution detection with subspace techniques and probabilistic modeling of features,”
Ibrahima Ndiour, Nilesh Ahuja, and Omesh Tickoo, · 2020
Earlier work this paper cites.
“Prevalence of neural collapse during the terminal phase of deep learning training,”
Vardan Papyan, XY Han, and David L Donoho, · 2020
Cited alongside, same era.
“React: Out-of-distribution detection with rectified activations,”
Yiyou Sun, Chuan Guo, and Yixuan Li, · 2021
Cited alongside, same era.
“Neural collapse under mse loss: Proximity to and dynamics on the central path,”
XY Han, Vardan Papyan, and David L Donoho, · 2021
Cited alongside, same era.
“A geometric analysis of neural collapse with unconstrained features,”
Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, and Qing Qu, · 2021
Cited alongside, same era.
“Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation,”
Robin Chan, Matthias Rottmann, and Hanno Gottschalk, · 2021
Cited alongside, same era.
“Repvgg: Making vgg-style convnets great again,”
“Mitigating neural network overconfidence with logit normalization,”
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li, · 2022
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“Vim: Out-of-distribution with virtual-logit matching,”
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang, · 2022
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“incdfm: Incremental deep feature modeling for continual novelty detection,”
Amanda Rios, Nilesh Ahuja, Ibrahima Ndiour, Utku Genc, Laurent Itti, and Omesh Tickoo, · 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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“On the role of neural collapse in transfer learning,”
Tomer Galanti, András György, and Marcus Hutter, · 2022
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“Neural collapse inspired feature-classifier alignment for few-shot class-incremental learning,”
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Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun, · 2021
Cited alongside, same era.
“Swin transformer: Hierarchical vision transformer using shifted windows,”
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo, · 2021
Cited alongside, same era.
“Natural adversarial examples,”
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song, · 2021
Cited alongside, same era.
“Scaling out-of-distribution detection for real-world settings,”
Steven Basart, Mazeika Mantas, Mostajabi Mohammadreza, Steinhardt Jacob, and Song Dawn, · 2022
Cited alongside, same era.
Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip Torr, and Dacheng Tao, · 2022
Later among the works it cites.
“Linking neural collapse and l2 normalization with improved out-of-distribution detection in deep neural networks,”
Jarrod Haas, William Yolland, and Bernhard T Rabus, · 2022
Later among the works it cites.
“Decoupling maxlogit for out-of-distribution detection,”
Zihan Zhang and Xiang Xiang, · 2023
Later among the works it cites.
“Gen: Pushing the limits of softmax-based out-of-distribution detection,”
Xixi Liu, Yaroslava Lochman, and Christopher Zach, · 2023
Later among the works it cites.