Fetching the paper…
Reading the bibliography…
We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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
A Krizhevsky · 2009
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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.
Anomaly detection using deep learning based image completion
Matthias Haselmann, Dieter P Gruber, and ul Tabatabai · 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
Earlier work this paper cites.
Latent space autoregression for novelty detection
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2019
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
Cited alongside, same era.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel · 2019
Cited alongside, same era.
Ocgan: One-class novelty detection using gans with constrained latent representations
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang · 2019
Cited alongside, same era.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
Cited alongside, same era.
Hrn: A holistic approach to one class learning
Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, and Bing Liu · 2020
Cited alongside, same era.
Multiresolution knowledge distillation for anomaly detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, and Hamid R Rabiee · 2021
Later among the works it cites.
Learning and evaluating representations for deep one-class classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister · 2021
Later among the works it cites.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
Later among the works it cites.
Reconstruction by inpainting for visual anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
Later among the works it cites.
Deep one-class classification via interpolated gaussian descriptor
Yuanhong Chen, Yu Tian, Guansong Pang, and Gustavo Carneiro · 2022
Later among the works it cites.
Anomaly detection via reverse distillation from one-class embedding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Explainable deep one-class classification
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus Robert Muller · 2020
Cited alongside, same era.
Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
Cited alongside, same era.
Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection
Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, and Hong Zhou · 2021
Cited alongside, same era.
Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
Cited alongside, same era.
Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
Cited alongside, same era.
Hanqiu Deng and Xingyu Li · 2022
Later among the works it cites.
Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka · 2022
Later among the works it cites.
Self-supervised predictive convolutional attentive block for anomaly detection
Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B Moeslund, and Mubarak Shah · 2022
Later among the works it cites.
Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
Later among the works it cites.
Fully convolutional cross-scale-flows for image-based defect detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
Later among the works it cites.