Results 21 to 30 of about 30,442 (292)
CoRR, 2020 High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by pushing it through a neural network with a low dimension bottleneck while minimizing a reconstruction error.
Matan Atzmon, Amos Gropp, Yaron Lipman
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Autoencoding With a Classifier System [PDF]
IEEE Transactions on Evolutionary Computation, 2021 Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost.
Richard John Preen +2 more
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Journal of Cybersecurity and Privacy, 2023 An intrusion detection system (IDS), whether as a device or software-based agent, plays a significant role in networks and systems security by continuously monitoring traffic behaviour to detect malicious activities.
Fatemeh Ahmadi Abkenari +2 more
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, 2021 A Neural Network is a statistical tool that aids in the classification of data points for machine learning. Autoencoders are a type of neural network that are capable of learning large amounts of data without being supervised by labeled training data ...
Davidson, Shirley
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Topological obstructions to autoencoding [PDF]
Journal of High Energy Physics, 2021 Abstract Autoencoders have been proposed as a powerful tool for model-independent anomaly detection in high-energy physics. The operating principle is that events which do not belong to the space of training data will be reconstructed poorly, thus flagging them as anomalies.
Joshua Batson +3 more
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Deep Learning for Optical Sensor Applications: A Review
Sensors, 2023 Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors.
Nagi H. Al-Ashwal +3 more
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CoRR, 2017 In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Alireza Makhzani, Brendan J. Frey
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Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders
Sensors, 2019 Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data.
Weihua Jin +4 more
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IEEE Access, 2021 Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual
Chathurika S. Wickramasinghe +2 more
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Applied Sciences, 2020 As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image ...
Young-Joo Han, Ha-Jin Yu
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