Results 1 to 10 of about 113,519 (298)
, 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
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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
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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
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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
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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
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Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
Frontiers in Genetics, 2022 The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics.
Yishuai Geng +3 more
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RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data
Journal of Biological Engineering, 2023 Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets.
Ahmed Arafa +3 more
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Autoencoder untuk Sistem Prediksi Berat Lahir Bayi
Jurnal Teknologi Informasi dan Ilmu Komputer, 2022 Salah satu ukuran terpenting saat awal persalinan adalah keakuratan prediksi berat lahir. Dengan menggunakan metode prediksi yang tepat, perkiraan ekstrim berat lahir bayi dapat dideteksi lebih atau kurang sehingga beberapa tindakan pencegahan dapat ...
Fitra Septia Nugraha +1 more
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Anomaly Detection for Agricultural Vehicles Using Autoencoders
Sensors, 2022 The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this
Esma Mujkic +4 more
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Denoising Adversarial Autoencoders [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.
Antonia Creswell, Anil Anthony Bharath
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