Fetching the paper…
Reading the bibliography…
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
Earlier work this paper cites.
Letter to the editor—a monte carlo method for the approximate solution of certain types of constrained optimization problems
Martin Pincus · 1970
Earlier work this paper cites.
Identification of outliers
Douglas M Hawkins · 1980
Earlier work this paper cites.
Optimization by simulated annealing
Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi · 1983
Earlier work this paper cites.
Learning Internal Representations by Error Propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
The manifold ways of perception
H Sebastian Seung and Daniel D Lee · 2000
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson · 2001
Earlier work this paper cites.
A classification framework for anomaly detection
Ingo Steinwart, Don Hush, and Clint Scovel · 2005
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.
Learning multiple layers of features from tiny images
A Krizhevsky · 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.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Earlier work this paper cites.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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
Cited alongside, same era.
Constructing unrestricted adversarial examples with generative models
Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf · 2019
Cited alongside, same era.
Explore the transformation space for adversarial images
Jiyu Chen, David Wang, and Hao Chen · 2020
Later among the works it cites.
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, and Balaji Lakshminarayanan · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
ContraGAN: Contrastive Learning for Conditional Image Generation
Minguk Kang and Jaesik Park · 2020
Later among the works it cites.
Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, and Jordi Luque · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Analyzing the robustness of open-world machine learning
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, and Prateek Mittal · 2019
Cited alongside, same era.
Sampling can be faster than optimization
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, and Michael I Jordan · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Semantic adversarial attacks: Parametric transformations that fool deep classifiers
Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, and Chinmay Hegde · 2019
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Toyadmos: A dataset of miniature-machine operating sounds for anomalous sound detection
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, and Keisuke Imoto · 2019
Cited alongside, same era.
Charline Le Lan and Laurent Dinh · 2020
Later among the works it cites.
From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
Later among the works it cites.
Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit · 2020
Later among the works it cites.
Atom: Robustifying out-of-distribution detection using outlier mining
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 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.
{SSD}: A unified framework for self-supervised outlier detection
Vikash Sehwag, Mung Chiang, and Prateek Mittal · 2021
Later among the works it cites.
Random transformation of image brightness for adversarial attack
Bo Yang, Kaiyong Xu, Hengjun Wang, and Hengwei Zhang · 2021
Later among the works it cites.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
Later among the works it cites.
Autoencoding under normalization constraints
Sangwoong Yoon, Yung-Kyun Noh, and Frank Park · 2021
Later among the works it cites.
Collision detection for robot manipulators using unsupervised anomaly detection algorithms
Kyu Min Park, Younghyo Park, Sangwoong Yoon, and Frank C Park · 2021
Later among the works it cites.
Rodd: A self-supervised approach for robust out-of-distribution detection
Umar Khalid, Ashkan Esmaeili, Nazmul Karim, and Nazanin Rahnavard · 2022
Closest in time.
Adversarial vulnerability of powerful near out-of-distribution detection
Stanislav Fort · 2022
Closest in time.