Fetching the paper…
Reading the bibliography…
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
Wordnet: a lexical database for english
George A Miller · 1995
Earlier work this paper cites.
The case against accuracy estimation for comparing induction algorithms
Foster J Provost, Tom Fawcett, Ron Kohavi, et al · 1998
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 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.
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.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
Earlier work this paper cites.
Toward open set recognition
Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult · 2012
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Earlier work this paper cites.
Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 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.
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 Rayadurgam Srikant · 2018
Earlier work this paper cites.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
Earlier work this paper cites.
Open set learning with counterfactual images
Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, and Fuxin Li · 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.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Earlier work this paper cites.
A discussion of ’adversarial examples are not bugs, they are features’: Adversarial example researchers need to expand what is meant by ’robustness’
Justin Gilmer and Dan Hendrycks · 2019
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Earlier work this paper cites.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
Deep transfer learning for multiple class novelty detection
Pramuditha Perera and Vishal M Patel · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Unsupervised out-of-distribution detection by maximum classifier discrepancy
Qing Yu and Kiyoharu Aizawa · 2019
Cited alongside, same era.
Detecting semantic anomalies
Faruk Ahmed and Aaron Courville · 2020
Cited alongside, same era.
React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
Later among the works it cites.
No true state-of-the-art? ood detection methods are inconsistent across datasets
Fahim Tajwar, Ananya Kumar, Sang Michael Xie, and Percy Liang · 2021
Later among the works it cites.
Vos: Learning what you don’t know by virtual outlier synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li · 2022
Later among the works it cites.
Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
Later among the works it cites.
Improving out-of-distribution detection by learning from the deployment environment
Nathan Inkawhich, Jingyang Zhang, Eric K. Davis, Ryan Luley, and Yiran Chen · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
AugMix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 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.
Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2020
Cited alongside, same era.
The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John D Owens, and Yixuan Li · 2020
Cited alongside, same era.
Pytorch-ood: A library for out-of-distribution detection based on pytorch
Konstantin Kirchheim, Marco Filax, and Frank Ortmeier · 2022
Later among the works it cites.
Using mixup as a regularizer can surprisingly improve accuracy & out-of-distribution robustness
Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip Torr, and Puneet K. Dokania · 2022
Later among the works it cites.
Rankfeat: Rank-1 feature removal for out-of-distribution detection
Yue Song, Nicu Sebe, and Wei Wang · 2022
Later among the works it cites.
Dice: Leveraging sparsification for out-of-distribution detection
Yiyou Sun and Sharon Li · 2022
Later among the works it cites.
Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
Later among the works it cites.
Open-set recognition: A good closed-set classifier is all you need
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
Later among the works it cites.
Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 2022
Later among the works it cites.
Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
Later among the works it cites.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
Later among the works it cites.
Privacy leakage of adversarial training models in federated learning systems
Jingyang Zhang, Yiran Chen, and Hai Li · 2022
Later among the works it cites.
In or out? fixing imagenet out-of-distribution detection evaluation
Julian Bitterwolf, Maximilian Müller, and Matthias Hein · 2023
Closest in time.
Extremely simple activation shaping for out-of-distribution detection
Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, and Rosanne Liu · 2023
Closest in time.
A framework for benchmarking class-out-of-distribution detection and its application to imagenet
Ido Galil, Mohammed Dabbah, and Ran El-Yaniv · 2023
Closest in time.
Adversarial attacks on foundational vision models
Nathan Inkawhich, Gwendolyn McDonald, and Ryan Luley · 2023
Closest in time.
Robo3d: Towards robust and reliable 3d perception against corruptions
Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu · 2023
Closest in time.
How does fine-tuning impact out-of-distribution detection for vision-language models?
Yifei Ming and Yixuan Li · 2023
Closest in time.
How to exploit hyperspherical embeddings for out-of-distribution detection?
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
Closest in time.
Non-parametric outlier synthesis
Leitian Tao, Xuefeng Du, Jerry Zhu, and Yixuan Li · 2023
Closest in time.