Fetching the paper…
Reading the bibliography…
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 1901
Earlier work this paper cites.
Procedures for detecting outlying observations in samples
Frank E Grubbs · 1969
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 · 1999
Earlier work this paper cites.
Learning with Kernels
Bernhard Schölkopf and Alex J Smola · 2002
Earlier work this paper cites.
Parzen-window network intrusion detectors
Dit-Yan Yeung and Calvin Chow · 2002
Earlier work this paper cites.
Applications of hidden Markov models to detecting multi-stage network attacks
Dirk Ourston, Sara Matzner, William Stump, and Bryan Hopkins · 2003
Earlier work this paper cites.
Active learning for anomaly and rare-category detection
Dan Pelleg and Andrew W Moore · 2005
Earlier work this paper cites.
A classification framework for anomaly detection
Ingo Steinwart, Don Hush, and Clint Scovel · 2005
Earlier work this paper cites.
Rethinking assumptions in deep anomaly detection
Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, and Marius Kloft · 2006
Earlier work this paper cites.
Towards a learning traffic incident detection system
Tomas Singliar and Milos Hauskrecht · 2006
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.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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 and Geoffrey Hinton · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Anomaly detection in crowded scenes
Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Systematic construction of anomaly detection benchmarks from real data
Andrew F Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, and Weng-Keen Wong · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
Fast R-CNN
Ross Girshick · 2015
Earlier work this paper cites.
Deep convolutional inverse graphics network
Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
Earlier work this paper cites.
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Sarah M Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 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.
Deep structured energy based models for anomaly detection
Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang · 2016
Earlier work this paper cites.
Yoshua Bengio · 2017
Earlier work this paper cites.
Universal representations: The missing link between faces, text, planktons, and cat breeds
Hakan Bilen and Andrea Vedaldi · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Cited alongside, same era.
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 · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
Cited alongside, same era.
Advances in pre-training distributed word representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin · 2018
Later among the works it cites.
Generative probabilistic novelty detection with adversarial autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, and Gianfranco Doretto · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
S-A. Rebuffi, H. Bilen, and A. Vedaldi · 2018
Later among the works it cites.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Later among the works it cites.
Adversarially learned one-class classifier for novelty detection
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli · 2018
Later among the works it cites.
Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C Paffenroth · 2017
Cited alongside, same era.
GANomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2018
Cited alongside, same era.
Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2018
Cited alongside, same era.
Later among the works it cites.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
Later among the works it cites.
A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2018
Later among the works it cites.
Deep autoencoding Gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
Later among the works it cites.
Latent space autoregression for novelty detection
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2019
Later among the works it cites.
Docbert: Bert for document classification
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, and Jimmy Lin · 2019
Later among the works it cites.
Faruk Ahmed and Aaron Courville · 2019
Later among the works it cites.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
Later among the works it cites.
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
Later among the works it cites.
Spottune: transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
Later among the works it cites.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
Later among the works it cites.
OCGAN: One-class novelty detection using gans with constrained latent representations
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang · 2019
Later among the works it cites.
BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray · 2019
Later among the works it cites.
Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
Later among the works it cites.
Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2020
Closest in time.
Deep nearest neighbor anomaly detection
Liron Bergman, Niv Cohen, and Yedid Hoshen · 2020
Closest in time.
Latent domain learning with dynamic residual adapters
Lucas Deecke, Hospedales Timothy, and Hakan Bilen · 2020
Closest in time.
Pretrained transformers improve out-of-distribution robustness
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song · 2020
Closest in time.
Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
Closest in time.