3. Anomaly Detection & One-Class Novelty Detection

<a name="top"></a> # 3. Anomaly Detection & One-Class Novelty Detection - [3.1 Density-based Methods](#3.1) - [3.1.1 Classic Density Estimation](#3.1.1) - [3.1.2 NN-based Density Estimation](#3.1.2) - [3.1.3 Energy-based Models](#3.1.3) - [3.1.4 Frequency-based Methods](#3.1.4) - [3.2 Reconstruction-based Methods](#3.2) - [3.2.1 Sparse Representation Methods](#3.2.1) - [3.2.2 Reconstruction-Error Methods](#3.2.2) - [3.3 Classification-based Methods](#3.3) - [3.3.1 One-Class Classification](#3.3.1) - [3.3.2 Positive-Unlabeled Learning](#3.3.2) - [3.3.3 Self-Supervised Learning](#3.3.3) - [3.4 Distance-based Methods](#3.4) - [3.5 Gradient-based Methods](#3.5) - [3.6 Discussion and Theoretical Analysis](#3.6) <a name="3.1"></a> ## 3.1 Density-based Methods <a name="3.1.1"></a> ### 3.1.1 Classic Density Estimation ### **[TPAMI-1998]** [Parametric model fitting: from inlier characterization to outlier detection](https://ieeexplore.ieee.org/abstract/document/667884) **Authors:** Gaudenz Danuser, M. Stricker **Institution:** Marine Biological Laboratory; Analytical, and Mathematical Services **[JESP-2018]** [Detecting multivariate outliers: Use a robust variant of the mahalanobis distance](https://www.sciencedirect.com/science/article/abs/pii/S0022103117302123#!) **Authors:** Christophe Leys, Olivier Klein, Yves Dominicy **Institution:** University libre de Bruxelles; Ghent University **[ICML-2000]** [Anomaly detection over noisy data using learned probability distributions](https://academiccommons.columbia.edu/doi/10.7916/D8C53SKF) **Authors:** Eskin Eleazar **Institution:** Columbia University **[ISI-2016]** [Poisson factorization for peer-based anomaly detection](https://ieeexplore.ieee.org/abstract/document/7745472) **Authors:** Melissa Turcotte, Juston Moore, Nick Heard, Aaron McPhall **Institution:** Los Alamos National Laboratory; University of Bristol **[JASA-1991]** [Review papers: Recent developments in non-parametric density estimation](https://www.tandfonline.com/doi/abs/10.1080/01621459.1991.10475021) **Authors:** Alan Julian Izenman **Institution:** Temple University **[TKDE-2018]** [Anomaly detection using local kernel density estimation and context-based regression](https://ieeexplore.ieee.org/abstract/document/8540843) **Authors:** Weiming Hu, Jun Gao, Bing Li, Ou Wu, Junping Du, Stephen Maybank **Institution:** Chinese Academy of Sciences; University of Chinese Academy of Sciences; Tianjin University; Birkbeck College [Back to Top](#top) <a name="3.1.2"></a> ### 3.1.2 NN-based Density Est. ### **[ICLR-2018]** [Deep autoencoding gaussian mixture model for Deep autoencoding gaussian mixture model for unsupervised anomaly detection](https://openreview.net/forum?id=BJJLHbb0-) **Authors:** Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen **Institution:** Washington State University; NEC Laboratories America **[CVPR-2019]** [Latent Space Autoregression for Novelty Detection](https://openaccess.thecvf.com/content_CVPR_2019/html/Abati_Latent_Space_Autoregression_for_Novelty_Detection_CVPR_2019_paper.html) **Authors:** Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara **Institution:** University of Modena and Reggio Emilia **[NeurIPS-2018]** [Generative probabilistic novelty detection with adversarial autoencoders](https://arxiv.org/abs/1807.02588) **Authors:** Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto **Institution:** West Virginia University **[ECMLPKDD-2018]** [Image anomaly detection with generative adversarial networks](https://link.springer.com/chapter/10.1007/978-3-030-10925-7_1) **Authors:** Lucas Deecke, Robert VandermeulenLukas, RuffStephan Mandt, Marius Kloft **Institution:** University of EdinburghEdinburghScotland; TU Kaiserslautern; Hasso Plattner Institute; University of California **[ICML-2015]** [Variational inference with normalizing flows](http://proceedings.mlr.press/v37/rezende15.html) **Authors:** Danilo Rezende, Shakir Mohamed **Institution:** Google DeepMind > **[TPAMI-2020]** [Normalizing flows: An introduction and review of current methods](https://ieeexplore.ieee.org/abstract/document/9089305) **Authors:** Ivan Kobyzev, Simon J.D. Prince, Marcus A. Brubaker **Institution:** Borealis AI **[CVPR-2021]** [Cutpaste: Self-supervised learning for anomaly detection and localization](https://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_SelfSupervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html) **Authors:** Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister **Institution:** Google Cloud AI Research **[CVPR-2021]** [Multiresolution knowledge distillation for anomaly detection](https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html) **Authors:** Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, Hamid R. Rabiee **Institution:** Sharif University of Technology **[NeurIPS-2018]** [A loss framework for calibrated anomaly detection](https://proceedings.neurips.cc/paper/2018/file/959a557f5f6beb411fd954f3f34b21c3-Paper.pdf) **Authors:** Aditya Krishna Menon, Robert C. Williamson **Institution:** Australian National University **[CVPR-2021]** [Multiattentional deepfake detection](https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.html) **Authors:** Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu **Institution:** University of Science and Technology of China; Microsoft Cloud AI **[AAAI-2020]** [Ml-loo:Detecting adversarial examples with feature attribution](https://ojs.aaai.org/index.php/AAAI/article/view/6140) **Authors:** Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael Jordan **Institution:** University of California **[CIKM-2020]** [Towards generalizable deepfake detection with locality-aware autoencoder](https://dl.acm.org/doi/abs/10.1145/3340531.3411892) **Authors:** Mengnan Du, Shiva Pentyala, Yuening Li, Xia Hu **Institution:** Texas A&M University [Back to Top](#top) <a name="3.1.3"></a> ### 3.1.3 Energy-based Models ### **[ICML-2016]** [Deep structured energy based models for anomaly detection](http://proceedings.mlr.press/v48/zhai16.html) **Authors:** Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang **Institution:** Binghamton Univeristy; IBM T. J. Watson Research Center; Tsinghua University **[2005]** [Estimation of non-normalized statistical models by score matching](https://www.jmlr.org/papers/volume6/hyvarinen05a/hyvarinen05a.pdf) **Authors:** Aapo Hyv¡§arinen **Institution:** BHelsinki Institute for Information Technology **[ICML-2011]** [Bayesian learning via stochastic gradient langevin dynamics](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.441.3813&rep=rep1&type=pdf) **Authors:** Max Welling, Yee Whye Teh **Institution:** University of California; UCL [Back to Top](#top) <a name="3.1.4"></a> ### 3.1.4 Frequency-based Models ### **[CVPR-2020]** [High-frequency component helps explain the generalization of convolutional neural networks](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_High-Frequency_Component_Helps_Explain_the_Generalization_of_Convolutional_Neural_Networks_CVPR_2020_paper.html) **Authors:** Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing **Institution:** UCarnegie Mellon University **[CNeurIPS-2019]** [Adversarial examples are not bugs, they are features](https://arxiv.org/abs/1905.02175) **Authors:** Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry **Institution:** MIT **[ICCV-2021]** [Amplitudephase recombination: Rethinking robustness of convolutional neural networks in frequency domain](https://openaccess.thecvf.com/content/ICCV2021/html/Chen_AmplitudePhase_Recombination_Rethinking_Robustness_of_Convolutional_Neural_Networks_in_Frequency_ICCV_2021_paper.html) **Authors:** Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian **Institution:** Peking University; Beihang University; AI Application Research Center Huawei **[CVPR-2021]** [Spatial-phase shallow learning: rethinking face forgery detection in frequency domain](https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Spatial-Phase_Shallow_Learning_Rethinking_Face_Forgery_Detection_in_Frequency_Domain_CVPR_2021_paper.html) **Authors:** Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu **Institution:** University of Science and Technology of China; Alibaba Group [Back to Top](#top) <a name="3.2"></a> ## 3.2 Reconstruction-based Methods <a name="3.2.1"></a> ### 3.2.1 Sparse Representation [//]: 106 **[J. Signal Process. Syst.-2015]** [Sparse coding with anomaly detection](https://link.springer.com/article/10.1007/s11265-014-0913-0). **Authors:** Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin **Institution:** Technion [//]: 107 **[Multimedia Tools and Applications-2017]** [Anomaly detection using sparse reconstruction in crowded scenes](https://link.springer.com/article/10.1007/s11042-016-4115-6). **Authors:** Ang Li, Zhenjiang Miao, Yigang Cen, Yi Cen **Institution:** Beijing Jiaotong University, Beijing Key Laboratory, Minzu University of China [//]: 108 **[IEEE-2014]** [Adaptive Sparse Representations for Video Anomaly Detection](https://ieeexplore.ieee.org/abstract/document/6587741). **Authors:** Xuan Mo, Vishal Monga, Raja Bala, Zhigang Fan **Institution:** Pennsylvania State University [//]: 109 **[Pattern Recognition-2013]** [AticleL1 norm based kpca for novelty detection](https://www.sciencedirect.com/science/article/pii/S0031320312002877). **Authors:** Yingchao Xiao, Huangang Wanga, Wenli Xu, Junwu Zhou **Institution:** Tsinghua University, Beijing General Research Institute of Mining & Metallurgy [//]: 110 **[AAAI-2021]** [Lren: Low-rank embedded network for sample-free hyperspectral anomaly detection](https://www.aaai.org/AAAI21Papers/AAAI-766.JiangK.pdf). **Authors:** Kai Jiang, Weiying Xie, Jie Lei, Tao Jiang, Yunsong Li **Institution:** Xidian University [Back to Top](#top) <a name="3.2.2"></a> ### 3.2.2 Reconstruction-Error Methods [//]:89 **[NeurIPS-2018]** [Generative probabilistic novelty detection with adversarial autoencoders](https://arxiv.org/abs/1807.02588). **Authors:** Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto **Institution:** West Virginia University [//]: 111 **[Wireless Telecommunications Symposium-2018]** [Autoencoderbased network anomaly detection](https://ieeexplore.ieee.org/abstract/document/8363930). **Authors:** Zhaomin Chen, Chai Kiat Yeo, Bu Sung Lee, Chiew Tong Lau **Institution:** Nanyang Technological University [//]: 112 **[Special Lecture on IE-2015]** [Variational autoencoder based anomaly detection using reconstruction probability](http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf). **Authors:** J. An and S. Cho **Institution:** cannot open [//]: 113 **[ICLR-W-2018]** [Efficient GAN-Based Anomaly Detection](https://arxiv.org/abs/1802.06222). **Authors:** Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar **Institution:** CentraleSup¡äelec, Nanyang Technological University, Carnegie Mellon University, Institute for Infocomm Research [//]: 114 **[CVPR-2018]** [Future frame prediction for anomaly detection¨Ca new baseline](http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Future_Frame_Prediction_CVPR_2018_paper.html). **Authors:** Wen Liu, Weixin Luo, Dongze Lian, Shenghua Gao **Institution:** ShanghaiTech University [//]: 115 **[CVPR-2019]** [Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection](https://openaccess.thecvf.com/content_ICCV_2019/html/Gong_Memorizing_Normality_to_Detect_Anomaly_Memory-Augmented_Deep_Autoencoder_for_Unsupervised_ICCV_2019_paper.html). **Authors:** Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel **Institution:** University of Adelaide, Deakin University, University of Western Australia [//]: 116 **[CVPR-2020]** [Learning Memory Guided Normality for Anomaly Detection](https://arxiv.org/abs/2101.12382). **Authors:** Kevin Stephen, Varun Menon **Institution:** Department of Information Technology, Pune Institute of Computer Technology, New York University [//]: 117 **[ICLR-2020]** [Robust subspace recovery layer for unsupervised anomaly detection](https://arxiv.org/abs/1904.00152). **Authors:** Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman **Institution:** School of Mathematics University of Minnesota [//]: 118 **[AAAI-2021]** [Learning semantic context from normal samples for unsupervised anomaly detection](https://www.aaai.org/AAAI21Papers/AAAI-4221.YanX.pdf). **Authors:** Xudong Yan, Huaidong Zhang, Xuemiao Xu1, Xiaowei Hu, Pheng-Ann Heng **Institution:** South China University of Technology, Ministry of Education Key Laboratory of Big Data and Intelligent Robot, Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information [//]: 119 **[ICML-2019]** [Anomaly detection with multiple-hypotheses predictions](http://proceedings.mlr.press/v97/nguyen19b.html). **Authors:** Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox **Institution:** University of Freiburg, Germany Corporate Research [//]: 120 **[AAAI-2019]** [Learning competitive and discriminative reconstructions for anomaly detection](https://ojs.aaai.org/index.php/AAAI/article/view/4451). **Authors:** Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan **Institution:** Fudan University, University of North Carolina at Charlotte, Tongji University [//]: 121 **[CVPR-2018]** [Adversarially learned one-class classifier for novelty detection](https://openaccess.thecvf.com/content_cvpr_2018/html/Sabokrou_Adversarially_Learned_One-Class_CVPR_2018_paper.html). **Authors:** Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, Ehsan Adeli **Institution:** Institute for Research in Fundamental Sciences, Amirkabir University of Technology, Stanford University [//]: 122 **[IEEE/CVF-2019]** [Ocgan: One-class novelty detection using gans with constrained latent representations](http://openaccess.thecvf.com/content_CVPR_2019/html/Perera_OCGAN_One-Class_Novelty_Detection_Using_GANs_With_Constrained_Latent_Representations_CVPR_2019_paper.html). **Authors:** Pramuditha Perera, Ramesh Nallapati, Bing Xiang **Institution:** Johns Hopkins University, AWS AI [//]: 123 **[ECCV-2020]** [Encoding structure-texture relation with p-net for anomaly detection in retinal images](https://arxiv.org/pdf/2008.03632). **Authors:** Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao **Institution:** ShanghaiTech University, Chinese Academy of Sciences, Southern University, Shanghai Engineering Research Center of Intelligent Vision and Imaging [//]: 124 **[arXiv preprint arXiv-2020]** [Gan ensemble for anomaly detection](https://www.aaai.org/AAAI21Papers/AAAI-1883.HanX.pdf). **Authors:** Xu Han, Xiaohui Chen, Li-Ping Liu **Institution:** Tufts University [Back to Top](#top) <a name="3.3"></a> ## 3.1 Classification-based Methods <a name="3.3.1"></a> ### 3.3.1 One-Class Classification **[Journal of Artificial Intelligence Research-2002]** [One-class classification: Concept learning in the absence of counter-examples.](https://elibrary.ru/item.asp?id=5230402) **Authors:** Tax, David Martinus Johannes **Institution:** TU Delft **[ICML-2018]** [Deep one-class classification](http://proceedings.mlr.press/v80/ruff18a) **Authors:** Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke, Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and Muller, Emmanuel and Kloft, Marius **Institution:** Humboldt University of Berlin; Hasso Plattner Institute; TU Kaiserslautern; TU Berlin; University of Edinburgh; Singapore University of Technology and Design **[CVPR-2021]** [PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation](https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.html) **Authors:** Reiss, Tal and Cohen, Niv and Bergman, Liron and Hoshen, Yedid **Institution:** The Hebrew University of Jerusalem **[CVPR-2019]** [Gods: Generalized one-class discriminative subspaces for anomaly detection](https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_GODS_Generalized_One-Class_Discriminative_Subspaces_for_Anomaly_Detection_ICCV_2019_paper.html) **Authors:** Wang, Jue and Cherian, Anoop **Institution:** Australian National University; Mitsubishi Electric Research Labs [Back to Top](#top) <a name="3.3.2"></a> ### 3.3.2 Positive-Unlabeled Learning **[Machine Learning-2020]** [Learning from positive and unlabeled data: A survey](https://link.springer.com/article/10.1007/s10994-020-05877-5) **Authors:** Bekker, Jessa and Davis, Jesse **Institution:** KU Leuven **[International Symposiums on Information Processing-2008]** [Learning from positive and unlabeled examples: A survey](https://ieeexplore.ieee.org/abstract/document/4554167) **Authors:** Zhang, Bangzuo and Zuo, Wanli **Institution:** Jilin University; Northeast Normal University **[International Conference on Information, Intelligence, Systems and Applications-2019]** [Positive and unlabeled learning algorithms and applications: A survey](https://ieeexplore.ieee.org/abstract/document/8900698) **Authors:** Jaskie, Kristen and Spanias, Andreas **Institution:** Arizona State University **[IJCAI-2003]** [Learning to classify texts using positive and unlabeled data](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.9914&rep=rep1&type=pdf) **Authors:** Li, Xiaoli and Liu, Bing **Institution:** National University of Singapore; University of Illinois at Chicago **[Bioinformatics-2006]** [PSoL: a positive sample only learning algorithm for finding non-coding RNA genes](https://academic.oup.com/bioinformatics/article/22/21/2590/250725?login=true) **Authors:** Wang, Chunlin and Ding, Chris and Meraz, Richard F and Holbrook, Stephen R **Institution:** Lawrence Berkeley National Laboratory **[ICONIP-2012]** [Learning from positive and unlabelled examples using maximum margin clustering](https://link.springer.com/chapter/10.1007/978-3-642-34487-9_56) **Authors:** Chaudhari, Sneha and Shevade, Shirish **Institution:** IBM Research; Indian Institute of Science **[Journal of Computers-2009]** [Reliable Negative Extracting Based on kNN for Learning from Positive and Unlabeled Examples.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.415.7161&rep=rep1&type=pdf) **Authors:** Zhang, Bangzuo and Zuo, Wanli **Institution:** Jilin University; Northeast Normal University **[Journal of Information Science and Engineering-2014]** [Clustering-based Method for Positive and Unlabeled Text Categorization Enhanced by Improved TFIDF.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.684.162&rep=rep1&type=pdf) **Authors:** Liu, Lu and Peng, Tao **Institution:** University of Illinois at Urbana-Champaign Urbana; Jilin University **[arXiv-2018]** [Instance-dependent pu learning by bayesian optimal relabeling](https://arxiv.org/abs/1808.02180) **Authors:** He, Fengxiang and Liu, Tongliang and Webb, Geoffrey I and Tao, Dacheng **Institution:** University of Sydney **[AAAI-2019]** [Learning competitive and discriminative reconstructions for anomaly detection](https://ojs.aaai.org/index.php/AAAI/article/view/4451) **Authors:** Tian, Kai and Zhou, Shuigeng and Fan, Jianping and Guan, Jihong **Institution:** Fudan University; University of North Carolina; Tongji University **[CVPR-2019]** [Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection](https://openaccess.thecvf.com/content_CVPR_2019/html/Zhong_Graph_Convolutional_Label_Noise_Cleaner_Train_a_Plug-And-Play_Action_Classifier_CVPR_2019_paper.html) **Authors:** Zhong, Jia-Xing and Li, Nannan and Kong, Weijie and Liu, Shan and Li, Thomas H and Li, Ge **Institution:** Peking University **[ICML-2015]** [Learning from corrupted binary labels via class-probability estimation](http://proceedings.mlr.press/v37/menon15.html) **Authors:** Menon, Aditya and Van Rooyen, Brendan and Ong, Cheng Soon and Williamson, Bob **Institution:** National ICT Australia; The Australian National University **[Artificial Intelligence and Statistics-2015]** [A rate of convergence for mixture proportion estimation, with application to learning from noisy labels](http://proceedings.mlr.press/v38/scott15.html) **Authors:** Scott, Clayton **Institution:** University of Michigan [Back to Top](#top) <a name="3.3.3"></a> ### 3.3.3 Self-Supervised Learning **[ICDM-2008]** [Isolation forest](https://ieeexplore.ieee.org/abstract/document/4781136) **Authors:** Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua **Institution:** Monash University; Nanjing University **[NeurIPS-2018]** [Deep anomaly detection using geometric transformations](https://arxiv.org/abs/1805.10917) **Authors:** Golan, Izhak and El-Yaniv, Ran **Institution:** Israel Institute of Technology **[ICLR-2020]** [Classification-based anomaly detection for general data](https://arxiv.org/abs/2005.02359) **Authors:** Bergman, Liron and Hoshen, Yedid **Institution:** The Hebrew University of Jerusalem **[NeurIPS-2020]** [Csi: Novelty detection via contrastive learning on distributionally shifted instances](https://arxiv.org/abs/2007.08176) **Authors:** Tack, Jihoon and Mo, Sangwoo and Jeong, Jongheon and Shin, Jinwoo **Institution:** KAIST **[CVPR-2021]** [Anomaly detection in video via self-supervised and multi-task learning](https://openaccess.thecvf.com/content/CVPR2021/html/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.html) **Authors:** Georgescu, Mariana-Iuliana and Barbalau, Antonio and Ionescu, Radu Tudor and Khan, Fahad Shahbaz and Popescu, Marius and Shah, Mubarak **Institution:** University of Bucharest; Abu Dhabi; SecurifAI; University of Central Florida **[CVPR-2019]** [Object-centric auto-encoders and dummy anomalies for abnormal event detection in video](https://openaccess.thecvf.com/content_CVPR_2019/html/Ionescu_Object-Centric_Auto-Encoders_and_Dummy_Anomalies_for_Abnormal_Event_Detection_in_CVPR_2019_paper.html) **Authors:** Ionescu, Radu Tudor and Khan, Fahad Shahbaz and Georgescu, Mariana-Iuliana and Shao, Ling **Institution:** IIAI; University of Bucharest; SecurifAI [Back to Top](#top) <a name="3.4"></a> ## 3.4 Distance-based Methods **[PHM Society European Conference, 2014]** [Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm](https://papers.phmsociety.org/index.php/phme/article/download/1554/522) **Authors:** J. Tian, M. H. Azarian, and M. Pecht **Institution:** Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, U.S.A. **[GI/ITG Workshop MMBnet, pp. 13¨C14, 2007]** [Traffic anomaly detection using k-means clustering](https://www.net.in.tum.de/projects/dfg-lupus/files/muenz07k-means.pdf) **Authors:** G. Munz, S. Li, and G. Carle **Institution:** Wilhelm Schickard Institute for Computer Science; University of Tuebingen, Germany **[International conference on networked digital technologies, pp. 135¨C145, Springer,2012]** [Unsupervised clustering approach for network anomaly detection](https://eprints.soton.ac.uk/338221/1/Unsupervised_Clustering_and_Outlier_Detection_approach_for_network_anomaly_detection_-_camera_ready_new.pdf) **Authors:** I. Syarif, A. Prugel-Bennett, and G. Wills **Institution:** School of Electronics and Computer Science, University of Southampton, UK; Eletronics Engineering Polytechnics Institute of Surabaya, Indonesia <a name="3.5"></a> ## 3.5 Gradient-based Methods **[ECCV-2020]** [Back-propagated gradient representations for anomaly detection](https://arxiv.org/pdf/2007.09507) **Authors:** G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib **Institution:** Georgia Institute of Technology, Atlanta, GA 30332, USA <a name="3.6"></a> ## 3.6 Discussion and Theoretical Analysis **[ICML-2018]** [Open category detection with pac guarantees](http://proceedings.mlr.press/v80/liu18e/liu18e.pdf) **Authors:** S. Liu, R. Garrepalli, T. Dietterich, A. Fern, and D. Hendrycks **Institution:** Department of Statistics, Oregon State University, Oregn, USA School of EECS, Oregon State University, Oregon, USA University of California, Berkeley, California USA **[ICML-2021]** [Learning bounds for open-set learning](http://proceedings.mlr.press/v139/fang21c/fang21c.pdf) **Authors:** Z. Fang, J. Lu, A. Liu, F. Liu, and G. Zhang **Institution:** AAII, University of Technology Sydney.

Related Documents

Evaluation Harness (Offline + Online)

/godmode:eval

🔬 Open Deep Research

EEG-Datasets