Adversarially Learned Anomaly Detection

Zenati, Houssam; Romain, Manon; Foo, Chuan Sheng; Lecouat, Bruno; Chandrasekhar, Vijay

doi:10.48550/arxiv.1812.02288

Cited by 4 publications

(8 citation statements)

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“…The data encoding follows the static prescription described in 4.2. 20 The adversarial anomaly detection (ALAD) algorithm [113,114] is a hybrid method which combines generative adversarial networks [115] with autoencoders [74], designed for anomaly detection. Generative adversarial networks (GANs) are composed of two neural networks which compete against each other during training.…”

Section: Deep Autoencoding Gaussian Mixture Modelmentioning

confidence: 99%

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

et al. 2022

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We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1}10fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

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Section: Deep Autoencoding Gaussian Mixture Modelmentioning

confidence: 99%

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

et al. 2022

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“…(2) While this is not directly possible with GANs, since a generated G(z) doesn't correspond to a specific x, several GANbased solutions have been proposed that would be suitable for anomaly detection, as for instance in Refs. [18,20,[27][28][29].…”

Section: Adversarially Learned Anomaly Detectionmentioning

confidence: 99%

“…In this work, we focus on the ALAD method [18], built upon the use of bidirectional-GANs (BiGAN) [30]. As shown in Fig.…”

Section: Adversarially Learned Anomaly Detectionmentioning

confidence: 99%

“…In this paper, we extend the work of Ref. [3] in two directions: (i) we identify anomalies using an Adversarially Learned Anomaly Detection (ALAD) algorithm [18], which combines the strength of generative adversarial networks [19,20] with that of autoencoders [21][22][23]; (ii) we demonstrate how the anomaly detection would work in real life, using the ALAD algorithm to re-discover the top quark. To this purpose we use real LHC data, released by the CMS experiment on the CERN Open Data portal [24].…”

Section: Introductionmentioning

confidence: 99%

“…Our implementation of the ALAD model in TensorFlow [25], derived from the original code of Ref. [18], is available on GitHub [26]. This paper is structured as follows: the ALAD algorithm is described in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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