Results 21 to 30 of about 14,221 (268)

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, Bo Sun, Zhidong Li, Shijie Zhang, Zhonggui Chen   +4 more
doaj   +1 more source

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, Khaled A. M. Al Soufy, Mohga E. Hamza, Mohamed A. Swillam   +3 more
doaj   +1 more source

Structuring Autoencoders [PDF]

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019
In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desired structure in this low dimensional space. While traditional Autoencoders have proven to structure data naturally they fail to discover semantic structure that is hard to ...
Marco Rudolph, Bastian Wandt, Bodo Rosenhahn   +2 more
openaire   +2 more sources

Feedback Recurrent Autoencoder [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang Yang 0010, Guillaume Sautière, J. Jon Ryu, Taco S. Cohen   +3 more
openaire   +2 more sources

Architectural Design and Performance Trade-offs of Deep Learning-Based Autoencoders for Wireless Communication Systems [PDF]

JES: Journal of Engineering Sciences
Deep learning-based autoencoders have shown significant potential in wireless communication systems by enabling the joint optimization of modulation and coding schemes.
Mohammed Abo-Zahhad, Eman Ismail, Abdelhay Ali, Osama Elnahas   +3 more
doaj   +1 more source

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

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, Daniel L. Marino, Milos Manic   +2 more
doaj   +1 more source

Sinkhorn AutoEncoders

CoRR, 2018
Accepted for oral presentation at ...
Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav 0001, Max Welling, Tim Genewein, Frank Nielsen   +7 more
openaire   +4 more sources

AutoEncoder for Interpolation

CoRR, 2021
5 ...
openaire   +2 more sources

Simplex Autoencoders

CoRR, 2023
Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces.
Aymene Mohammed Bouayed, David Naccache
openaire   +2 more sources

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

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
doaj   +1 more source