Results 21 to 30 of about 75,818 (178)
Complex-valued autoencoders [PDF]
Neural Networks, 2012 Autoencoders are unsupervised machine learning circuits whose learning goal is to minimize a distortion measure between inputs and outputs. Linear autoencoders can be defined over any field and only real-valued linear autoencoder have been studied so far.
Baldi, Pierre, Lu, Zhiqin
openaire +4 more sources
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2023 This paper presents a new hybrid algorithm using multiple Support Vector Machines models with convolutional autoencoder to Electrical Impedance Tomography, and Ultrasound Computed Tomography image reconstruction.
Łukasz Maciura +3 more
doaj +1 more source
Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway
Applied Sciences, 2020 Recently, novelty detection with reconstruction along projection pathway (RaPP) has made progress toward leveraging hidden activation values. RaPP compares the input and its autoencoder reconstruction in hidden spaces to detect novelty samples ...
Seung Yeop Shin, Han-joon Kim
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Practical autoencoder based anomaly detection by using vector reconstruction error
Cybersecurity, 2023 Nowadays, cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network. Cloud computing services are available through common internet protocols and network standards. In addition
Hasan Torabi +2 more
semanticscholar +1 more source
Review on autoencoder and its application
Tongxin xuebao, 2021 As a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many ...
Jie LAI +4 more
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Energy Reports, 2021 Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed.
Hongteng Wang +3 more
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Semantic Autoencoder for Zero-Shot Learning [PDF]
Computer Vision and Pattern Recognition, 2017 Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g. attribute space).
E. Kodirov, T. Xiang, S. Gong
semanticscholar +1 more source
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates [PDF]
European Journal of Mechanics - A/Solids, 2020 In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order continuity of the DAEM and integrates a deep autoencoder and the minimum ...
X. Zhuang +3 more
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Unsupervised Outlier Detection via Transformation Invariant Autoencoder
IEEE Access, 2021 Autoencoder based methods are the majority of deep unsupervised outlier detection methods. However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high.
Zhen Cheng +4 more
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Cybersecurity, 2023 In recent years, many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks. The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices ...
B. S. Sharmila, Rohini Nagapadma
semanticscholar +1 more source