Fetching the paper…
Reading the bibliography…
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability.
The open world assumption
Nick Drummond and Rob Shearer · 2006
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.
Visual odometry based on stereo image sequences with ransac-based outlier rejection scheme
Bernd Kitt, Andreas Geiger, and Henning Lategahn · 2010
Earlier work this paper cites.
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 · 2017
Earlier work this paper cites.
OpenImages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al · 2017
Earlier work this paper 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
Earlier work this paper cites.
Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
Earlier work this paper cites.
Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
Earlier work this paper cites.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
Earlier work this paper cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Earlier work this paper cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 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.
MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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
Cited alongside, same era.
Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2019
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
Cited alongside, same era.
A less biased evaluation of out-of-distribution sample detectors
Alireza Shafaei, Mark Schmidt, and James J Little · 2019
Cited alongside, same era.
PyTorch image models
Ross Wightman · 2019
Cited alongside, same era.
Unsupervised out-of-distribution detection by maximum classifier discrepancy
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
Later among the works it cites.
Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
Later among the works it cites.
Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
Later among the works it cites.
On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
Later among the works it cites.
MOS: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Qing Yu and Kiyoharu Aizawa · 2019
Cited alongside, same era.
Outlier detection through null space analysis of neural networks
Matthew Cook, Alina Zare, and Paul Gader · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Big transfer (BiT): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
Cited alongside, same era.
Out-of-distribution detection with subspace techniques and probabilistic modeling of features
Ibrahima Ndiour, Nilesh Ahuja, and Omesh Tickoo · 2020
Cited alongside, same era.
Are open set classification methods effective on large-scale datasets?
Ryne Roady, Tyler L Hayes, Ronald Kemker, Ayesha Gonzales, and Christopher Kanan · 2020
Cited alongside, same era.
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
Later among the works it cites.
ReAct: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Later among the works it cites.
NGC: A unified framework for learning with open-world noisy data
Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, and Yu-Feng Li · 2021
Later among the works it cites.
Semantically coherent out-of-distribution detection
Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, and Ziwei Liu · 2021
Later among the works it cites.
Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
Later among the works it cites.
Out-of-distribution detection using union of 1-dimensional subspaces
Alireza Zaeemzadeh, Niccolò Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, and Mubarak Shah · 2021
Later among the works it cites.