Results 81 to 90 of about 4,772,239 (326)
Anomaly Detection Using Autoencoder With Feature Vector Frequency Map
IEEE Access, 2021 Anomaly detection uses various machine learning techniques to identify and classify defective data on the production line. The autoencoder-based anomaly detection method is an unsupervised method that classifies abnormal samples using an autoencoder ...
Young-Gyu Kim, Tae-Hyoung Park
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Variational autoencoder(VAE), the learning process algorithm [82].
, 2023 Variational autoencoder(VAE), the learning process algorithm [82].
Zahra Rahaie (16641660) +2 more
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Computer Methods in Biomechanics and Biomedical Engineering: Imaging & VisualizationIn this work, we investigate a new deep learning method for the reconstruction of blood velocity from 4D phase contrast MRI measurements based on variational autoencoders.
Bruno Sixou, Monica Sigovan
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An Introduction to Variational Autoencoders [PDF]
Foundations and Trends® in Machine Learning, 2019 Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.
Diederik P. Kingma, Max Welling
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Variations in Variational Autoencoders - A Comparative Evaluation [PDF]
IEEE Access, 2020 Variational Auto-Encoders (VAEs) are deep latent space generative models which have been immensely successful in many applications such as image generation, image captioning, protein design, mutation prediction, and language models among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data can
Ruoqi Wei +4 more
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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
IEEE Access, 2022 With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field.
Lin Wang +4 more
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CoRR, 2016 Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture.
Xi Chen 0022 +7 more
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New Perturbed Proximal Point Algorithms for Set-valued Quasi Variational Inclusions [PDF]
, 2004 In this paper, by using some new and innovative techniques, some perturbed iterative algorithms for solving generalized set-valued variational inclusions are suggested and analyzed.
Li, Rong, Shi, Chaofeng
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β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Nature CommunicationsVariational autoencoder architectures have the potential to develop reduced-order models for chaotic fluid flows. We propose a method for learning compact and near-orthogonal reduced-order models using a combination of a β-variational autoencoder and a ...
Alberto Solera-Rico +6 more
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IEEE AccessThis article presents a new regularization of the latent space of a variational autoencoder that facilitates the visual selection of specific emotions of generated monophonic musical sequences.
Jacek Grekow
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