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Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Individual comparisons by ranking methods
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K-sparse autoencoders
Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan R Salakhutdinov · 2017
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Unsupervised and semi-supervised anomaly detection with LSTM neural networks
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Exploring generalization in deep learning
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On the expressive power of deep neural networks
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Alireza Makhzani and Brendan Frey · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Sergey Ioffe and Christian Szegedy · 2015
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A hybrid semi-supervised anomaly detection model for high-dimensional data
Hongchao Song, Zhuqing Jiang, Aidong Men, and Bo Yang · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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GANomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2018
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Fixing a broken ELBO
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On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
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To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
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Anomaly detection using one-class neural networks
Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla · 2018
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Image anomaly detection with generative adversarial networks
Lucas Deecke, Robert A Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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Invariant information distillation for unsupervised image segmentation and clustering
Xu Ji, Joao F Henriques, and Andrea Vedaldi · 2018
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An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
B Kiran, Dilip Thomas, and Ranjith Parakkal · 2018
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SU-IDS: A semi-supervised and unsupervised framework for network intrusion detection
Erxue Min, Jun Long, Qiang Liu, Jianjing Cui, Zhiping Cai, and Junbo Ma · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J Goodfellow · 2018
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Deep one-class classification
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib A Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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On the information bottleneck theory of deep learning
Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox · 2018
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A mathematical theory of deep convolutional neural networks for feature extraction
Thomas Wiatowski and Helmut Bölcskei · 2018
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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
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