Results 1 to 10 of about 75,818 (178)
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
EnergiesAnomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies.
Fadhila Lachekhab +4 more
doaj +2 more sources
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning [PDF]
IEEE International Conference on Computer Vision, 2022 Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales.
Colorado Reed +8 more
semanticscholar +1 more source
EEG-Based Personal Identification by Special Design Domain-Adaptive Autoencoder [PDF]
SensorsIndividual brain activity patterns derived from electroencephalogram (EEG) data offer a unique source for personal identification, introducing a novel approach to the field.
Muhammed Esad Oztemel +1 more
doaj +2 more sources
In-context Autoencoder for Context Compression in a Large Language Model [PDF]
International Conference on Learning Representations, 2023 We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes.
Tao Ge +4 more
semanticscholar +1 more source
, 2023 [en] In this project we study antoencoders, a machine learning tecnique used for dimensionality reduction of databases, analizing images or generating new data. We compare them with tradicional dimensionality reduction method, the principal component analysis (PCA).
Christopher M. Bishop, Hugh Bishop
+7 more sources
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training [PDF]
EAI Endorsed Transactions on Pervasive Health and Technology, 2020 INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology.
Saravanan. S, Sujitha Juliet
doaj +1 more source
Abnormal Network Traffic Detection Method Combining Mahalanobis Distance and Autoencoder [PDF]
Jisuanji gongcheng, 2022 The existing abnormal traffic detection methods are limited in the accuracy due to the large scale of network traffic data and its imbalanced distribution.To address the problem, a method combining Mahalanobis distance and autoencoder is proposed to ...
LI Beibei, PENG Li, DAI Feifei
doaj +1 more source
Biological Cybernetics, 2022 AbstractIn Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least ...
Hervé Bourlard, Selen Hande Kabil
openaire +3 more sources
Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images
Journal of Imaging, 2022 A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using ...
Elena Solovyeva, Ali Abdullah
doaj +1 more source
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection [PDF]
IEEE International Conference on Computer Vision, 2019 Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for ...
Dong Gong +6 more
semanticscholar +1 more source