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We aim for image-based novelty detection.
Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2002
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Generative openmax for multi-class open set classification
ZongYuan Ge, Sergey Demyanov, Zetao Chen, and Rahil Garnavi · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Multi-class weather dataset for image classification
A Gbeminiyi · 2018
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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Open set learning with counterfactual images
Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, and Fuxin Li · 2018
Earlier work this paper cites.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Fast and robust segmentation of white blood cell images by self-supervised learning
Xin Zheng, Yong Wang, Guoyou Wang, and Jianguo Liu · 2018
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Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Deep anomaly detection for generalized face anti-spoofing
Daniel Pérez-Cabo, David Jiménez-Cabello, Artur Costa-Pazo, and Roberto J López-Sastre · 2019
Cited alongside, same era.
Deep semi-supervised anomaly detection
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft · 2019
Cited alongside, same era.
Deep nearest neighbor anomaly detection
Liron Bergman, Niv Cohen, and Yedid Hoshen · 2020
Cited alongside, same era.
Transformaly–two (feature spaces) are better than one
Matan Jacob Cohen and Shai Avidan · 2021
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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
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Densely connected normalizing flows
Matej Grcić, Ivan Grubišić, and Siniša Šegvić · 2021
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Opengan: Open-set recognition via open data generation
Shu Kong and Deva Ramanan · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
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G2d: generate to detect anomaly
Masoud Pourreza, Bahram Mohammadi, Mostafa Khaki, Samir Bouindour, Hichem Snoussi, and Mohammad Sabokrou · 2021
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
Cited alongside, same era.
History-based anomaly detector: An adversarial approach to anomaly detection
Pierrick Chatillon and Coloma Ballester · 2020
Cited alongside, same era.
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, et al · 2020
Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Puzzle-ae: Novelty detection in images through solving puzzles
Mohammadreza Salehi, Ainaz Eftekhar, Niousha Sadjadi, Mohammad Hossein Rohban, and Hamid R Rabiee · 2020
Cited alongside, same era.
Later among the works it cites.
Mean-shifted contrastive loss for anomaly detection
Tal Reiss and Yedid Hoshen · 2021
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Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen · 2021
Later among the works it cites.
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
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Open-set recognition: A good closed-set classifier is all you need
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2021
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Do we really need to learn representations from in-domain data for outlier detection?
Zhisheng Xiao, Qing Yan, and Yali Amit · 2021
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Learning placeholders for open-set recognition
Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2021
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Studiogan: A taxonomy and benchmark of gans for image synthesis
Minguk Kang, Joonghyuk Shin, and Jaesik Park · 2022
Closest in time.
Algan: Anomaly detection by generating pseudo anomalous data via latent variables
Hironori Murase and Kenji Fukumizu · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
Closest in time.