Fetching the paper…
Reading the bibliography…
Machine learning models often encounter samples that are diverged from the training distribution.
Procedures for detecting outlying observations in samples
Frank E Grubbs · 1969
Earlier work this paper cites.
Statistical inference using extreme order statistics
James Pickands III et al · 1975
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
object image library (coil-100
Sameer A. Nene, Shree K. Nayar, and Hiroshi Murase · 1996
Earlier work this paper cites.
Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alexander J Smola, John Shawe-Taylor, John C Platt, et al · 1999
Earlier work this paper cites.
Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
Earlier work this paper cites.
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Thomas M. Cover and Joy A. Thomas · 2006
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
Earlier work this paper cites.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 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.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
Solving structured sparsity regularization with proximal methods
Sofia Mosci, Lorenzo Rosasco, Matteo Santoro, Alessandro Verri, and Silvia Villa · 2010
Earlier work this paper cites.
Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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.
Monitor alarm fatigue: an integrative review
Maria Cvach · 2012
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Off-line nepali handwritten character recognition using multilayer perceptron and radial basis function neural networks
Ashok Kumar Pant, Sanjeeb Prasad Panday, and Shashidhar Ram Joshi · 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.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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.
Semi-supervised learning with ladder networks
Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko · 2015
Earlier work this paper cites.
Learning discriminative reconstructions for unsupervised outlier removal
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
Earlier work this paper cites.
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
Nist special database 19 handprinted forms and characters 2nd edition
Patrick Grother and Kayee Hanaoka · 2016
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.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
How to train deep variational autoencoders and probabilistic ladder networks
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Fast feature extraction with cnns with pooling layers
Christian Bailer, Tewodros Habtegebrial, Didier Stricker, et al · 2017
Earlier work this paper cites.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Generative openmax for multi-class open set classification
ZongYuan Ge, Sergey Demyanov, Zetao Chen, and Rahil Garnavi · 2017
Earlier work this paper cites.
Nearest neighbors distance ratio open-set classifier
Pedro R Mendes Júnior, Roberto M De Souza, Rafael de O Werneck, Bernardo V Stein, Daniel V Pazinato, Waldir R de Almeida, Otávio AB Penatti, Ricardo da S Torres, and Anderson Rocha · 2017
Earlier work this paper cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
Earlier work this paper cites.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Adversarial robustness: Softmax versus openmax
Andras Rozsa, Manuel Günther, and Terrance E Boult · 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.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Earlier work this paper cites.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C Paffenroth · 2017
Earlier work this paper cites.
Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot, Colin Raffel, Aurko Roy, and Ian Goodfellow · 2018
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Earlier work this paper cites.
Generative ensembles for robust anomaly detection
Hyunsun Choi and Eric Jang · 2018
Cited alongside, same era.
Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance E Boult · 2018
Cited alongside, same era.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Cited alongside, same era.
Triplet-center loss for multi-view 3d object retrieval
Xinwei He, Yang Zhou, Zhichao Zhou, Song Bai, and Xiang Bai · 2018
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
Cited alongside, same era.
Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
Cited alongside, same era.
Background data resampling for outlier-aware classification
Yi Li and Nuno Vasconcelos · 2020
Later among the works it cites.
Explainable deep one-class classification
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus-Robert Müller · 2020
Later among the works it cites.
Few-shot scene-adaptive anomaly detection
Yiwei Lu, Frank Yu, Mahesh Kumar Krishna Reddy, and Yang Wang · 2020
Later among the works it cites.
Self-supervised learning for generalizable out-of-distribution detection
Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang · 2020
Later among the works it cites.
Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel · 2020
Later among the works it cites.
Generative-discriminative feature representations for open-set recognition
Pramuditha Perera, Vlad I Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, and Vishal M Patel · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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 · 2018
Cited alongside, same era.
Open set learning with counterfactual images
Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, and Fuxin Li · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Later among the works it cites.
Deep end-to-end one-class classifier
Mohammad Sabokrou, Mahmood Fathy, Guoying Zhao, and Ehsan Adeli · 2020
Later among the works it cites.
Puzzle-ae: Novelty detection in images through solving puzzles
Mohammadreza Salehi, Ainaz Eftekhar, Niousha Sadjadi, Mohammad Hossein Rohban, and Hamid R Rabiee · 2020
Later among the works it cites.
Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball, and Dan Zhang · 2020
Later among the works it cites.
Chexclusion: Fairness gaps in deep chest x-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2020
Later among the works it cites.
Open-set adversarial defense
Rui Shao, Pramuditha Perera, Pong C Yuen, and Vishal M Patel · 2020
Later among the works it cites.
Fairod: Fairness-aware outlier detection
Shubhranshu Shekhar, Neil Shah, and Leman Akoglu · 2020
Later among the works it cites.
Unsupervised anomaly detection with adversarial mirrored autoencoders
Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, and Soheil Feizi · 2020
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Later among the works it cites.
Conditional gaussian distribution learning for open set recognition
Xin Sun, Zhenning Yang, Chi Zhang, Keck-Voon Ling, and Guohao Peng · 2020
Later among the works it cites.
Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
Later among the works it cites.
Attention guided anomaly localization in images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2020
Later among the works it cites.
Towards fairness in visual recognition: Effective strategies for bias mitigation
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 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.
Old is gold: Redefining the adversarially learned one-class classifier training paradigm
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee · 2020
Later among the works it cites.
Hybrid models for open set recognition
Hongjie Zhang, Ang Li, Jie Guo, and Yanwen Guo · 2020
Later among the works it cites.
Learning deep classifiers consistent with fine-grained novelty detection
Jiacheng Cheng and Nuno Vasconcelos · 2021
Closest in time.
Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
Closest in time.
Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, and Hanie Sedghi · 2021
Closest in time.
Mos: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
Closest in time.
On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
Closest in time.
Oodformer: Out-of-distribution detection transformer
Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, and Volker Tresp · 2021
Closest in time.
Opengan: Open-set recognition via open data generation
Shu Kong and Deva Ramanan · 2021
Closest in time.
Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
Closest in time.
Provably robust detection of out-of-distribution data (almost) for free
Alexander Meinke, Julian Bitterwolf, and Matthias Hein · 2021
Closest in time.
Class anchor clustering: A loss for distance-based open set recognition
Dimity Miller, Niko Sunderhauf, Michael Milford, and Feras Dayoub · 2021
Closest in time.
One-class classification: A survey
Pramuditha Perera, Poojan Oza, and Vishal M Patel · 2021
Closest in time.
G2d: Generate to detect anomaly
Masoud Pourreza, Bahram Mohammadi, Mostafa Khaki, Samir Bouindour, Hichem Snoussi, and Mohammad Sabokrou · 2021
Closest in time.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Closest in time.
A simple fix to mahalanobis distance for improving near-ood detection
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 2021
Closest in time.
Modeling the distribution of normal data in pre-trained deep features for anomaly detection
Oliver Rippel, Patrick Mertens, and Dorit Merhof · 2021
Closest in time.
A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
Closest in time.
Ssd: A unified framework for self-supervised outlier detection
Vikash Sehwag, Mung Chiang, and Prateek Mittal · 2021
Closest in time.
Learning and evaluating representations for deep one-class classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister · 2021
Closest in time.
React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
Closest in time.
Medical transformer: Gated axial-attention for medical image segmentation
Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel · 2021
Closest in time.
Can multi-label classification networks know what they don’t know?
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li · 2021
Closest in time.
Learning semantic context from normal samples for unsupervised anomaly detection
Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Heng · 2021
Closest in time.
Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
Closest in time.
Understanding the effect of bias in deep anomaly detection
Ziyu Ye, Yuxin Chen, and Haitao Zheng · 2021
Closest in time.
Counterfactual zero-shot and open-set visual recognition
Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, and Hanwang Zhang · 2021
Closest in time.
Towards fair deep anomaly detection
Hongjing Zhang and Ian Davidson · 2021
Closest in time.
Learning placeholders for open-set recognition
Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2021
Closest in time.
Ood augmentation may be at odds with open-set recognition
Mohammad Azizmalayeri and Mohammad Hossein Rohban · 2022
Closest in time.
Your out-of-distribution detection method is not robust!
Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, and Mohammad Hossein Rohban · 2022
Closest in time.
Vos: Learning what you don’t know by virtual outlier synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li · 2022
Closest in time.
X-risk analysis for ai research
Dan Hendrycks and Mantas Mazeika · 2022
Closest in time.
Are out-of-distribution detection methods reliable?
Vahid Reza Khazaie, Anthony Wong, and Mohammad Sabokrou · 2022
Closest in time.
Exposing outlier exposure: What can be learned from few, one, and zero outlier images
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, and Marius Kloft · 2022
Closest in time.
On the impact of spurious correlation for out-of-distribution detection
Yifei Ming, Hang Yin, and Yixuan Li · 2022
Closest in time.
Fake it till you make it: Near-distribution novelty detection by score-based generative models
Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees GM Snoek, Mohammad Sabokrou, and Mohammad Hossein Rohban · 2022
Closest in time.
Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
Closest in time.
Open-set recognition: A good closed-set classifier is all you need
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
Closest in time.
Role of data augmentation in unsupervised anomaly detection
Jaemin Yoo, Tiancheng Zhao, and Leman Akoglu · 2022
Closest in time.