Results 61 to 70 of about 6,908 (119)
A Hybrid Autoencoder Network for Unsupervised Image Clustering
Algorithms, 2019 Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision.
Pei-Yin Chen, Jih-Jeng Huang
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Sensors, 2022 With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention.
Naoto Yoshimura +3 more
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Zooming Into Clarity: Image Denoising Through Innovative Autoencoder Architectures
IEEE AccessIn today’s era of increasing data complexity and pervasive noise, robust techniques for data processing, reconstruction, and denoising are crucial.
Khatereh Mohammadi +2 more
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Future Internet, 2022 Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital
Tan Nghia Duong +5 more
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Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest
Frontiers in OphthalmologyPurposeTo evaluate the diagnostic accuracy of a deep learning autoencoder-based model utilizing regions of interest (ROI) from optical coherence tomography (OCT) texture enface images for detecting glaucoma in myopic eyes.MethodsThis cross-sectional ...
Christopher Bowd +19 more
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CoRR, 2019 Enforcing distributions of latent variables in neural networks is an active subject. It is vital in all kinds of generative models, where we want to be able to interpolate between points in the latent space, or sample from it. Modern generative AutoEncoders (AE) like WAE, SWAE, CWAE add a regularizer to the standard (deterministic) AE, which allows to ...
Maciej Mikulski, Jaroslaw Duda 0001
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Autoencoding any Data through Kernel Autoencoders
CoRR, 2018 This paper investigates a novel algorithmic approach to data representation based on kernel methods. Assuming that the observations lie in a Hilbert space X, the introduced Kernel Autoencoder (KAE) is the composition of mappings from vector-valued Reproducing Kernel Hilbert Spaces (vv-RKHSs) that minimizes the expected reconstruction error.
Pierre Laforgue +2 more
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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.
Pierre Baldi, Zhiqin Lu
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An Enhanced Deep Autoencoder for Flight Delay Prediction
Journal of Aviation/Aerospace Education & ResearchAccurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems.
Desmond B Bisandu +2 more
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CoRR, 2018 In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning ...
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