adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection Article Swipe

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Xuhong Wang , Ying Du , Shijie Lin , Ping Cui , Yuntian Shen , Yupu Yang ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1016/j.knosys.2019.105187

Related Topics

Autoencoder

Overfitting

Anomaly Detection

Artificial Intelligence

Condensed Matter Physics

Quantum Mechanics

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Autoencoder Overfitting Anomaly detection Regularization (linguistics) Anomaly (physics) Artificial intelligence Gaussian Computer science Latent variable Pattern recognition (psychology) Generative model Generative grammar Machine learning Deep learning Artificial neural network Physics Condensed matter physics Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.knosys.2019.105187
OA Status: green
Cited By: 111
References: 94
Related Works: 10
OpenAlex ID: https://openalex.org/W2987821246

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DOI: https://doi.org/10.1016/j.knosys.2019.105187

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Title: adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

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Type: article

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Language: en

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Publication year: 2019

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Publication date: 2019-11-06

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Authors: Xuhong Wang, Ying Du, Shijie Lin, Ping Cui, Yuntian Shen, Yupu Yang

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Landing page: https://doi.org/10.1016/j.knosys.2019.105187

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Concepts: Autoencoder, Overfitting, Anomaly detection, Regularization (linguistics), Anomaly (physics), Artificial intelligence, Gaussian, Computer science, Latent variable, Pattern recognition (psychology), Generative model, Generative grammar, Machine learning, Deep learning, Artificial neural network, Physics, Condensed matter physics, Quantum mechanics

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Cited by: 111

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References (count): 94

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primary_location.raw_source_name	Knowledge-Based Systems
primary_location.landing_page_url	https://doi.org/10.1016/j.knosys.2019.105187
publication_date	2019-11-06
publication_year	2019
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referenced_works_count	94
abstract_inverted_index
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sustainable_development_goals[0].id	https://metadata.un.org/sdg/10
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sustainable_development_goals[0].display_name	Reduced inequalities
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