Search Results - Model-constrained autoencoder

TAEN: a model-constrained Tikhonov autoencoder network for forward and inverse problems by Van Nguyen, Hai, Bui-Thanh, Tan, Dawson, Clint

“… We propose a novel model-constrained Tikhonov autoencoder neural network framework, called TAEN, capable of learning both forward and inverse surrogate models using a single arbitrary observational sample…”
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TAE: A Model-Constrained Tikhonov Autoencoder Approach for Forward and Inverse Problems by Nguyen, Hai V, Bui-Thanh, Tan

“… We propose a novel Tikhonov autoencoder model-constrained framework, called TAE, capable of learning both forward and inverse surrogate models using a single arbitrary observation sample…”
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Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution by Gao, Lianru, Li, Jiaxin, Zheng, Ke, Jia, Xiuping

“…) to realize MS-aided HS-SR. First, two coupled autoencoders serve as the backbone of EU2ADL network to simultaneously decompose input modalities into abundances and corresponding endmembers, whose encoder part is composed…”
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Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning by Ibebuchi, Chibuike Chiedozie, Akinyemi, Oluwaferanmi, Abu, Itohan‐Osa

“… In this study, we present a novel approach utilizing autoencoder neural networks (AEs) combined with transfer learning to evaluate the representation of monthly sea level pressure (SLP…”
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Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection by Hu, Xing, Li, Zhixuan, Luo, Lingkun, Karimi, Hamid Reza, Zhang, Dawei

“…•Proposing an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection…”
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Embedding Consensus Autoencoder for Cross-modal Semantic Analysis by Sun, Sheng-Zi, Guo, Bing-Hui, Yang, Xiao-Bo

“… data.In this work, an Embedding Consensus Autoencoder for Cross-Modal Semantic Analysis is proposed, which maps the original data to a low-dimensional shared space to retain semantic…”
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Generative adversarial network constrained multiple loss autoencoder: A deep learning‐based individual atrophy detection for Alzheimer's disease and mild cognitive impairment by Shi, Rong, Sheng, Can, Jin, Shichen, Zhang, Qi, Zhang, Shuoyan, Zhang, Liang, Ding, Changchang, Wang, Luyao, Wang, Lei, Han, Ying, Jiang, Jiehui

“… Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE…”
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TCL: Time-Dependent Clustering Loss for Optimizing Post-Training Feature Map Quantization for Partitioned DNNs by Berg, Oscar Artur Bernd, Saqib, Eiraj, Jantsch, Axel, Shallari, Irida, Krug, Silvia, Sanchez Leal, Isaac, O'Nils, Mattias

“…This paper introduces an enhanced approach for deploying deep learning models on resource-constrained IoT devices by combining model partitioning, autoencoder-based compression, quantization with Time…”
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Physics-constrained scheme for outlier removal in wind turbine SCADA data for power curve modeling by Hong, Hoang Si, Hue, Nguyen Thi, Ninh, Nguyen Tuan, Thuan, Nguyen Duc, Huong, Nguyen Thi Lan

“…•The approach integrates density-based clustering and a physics-constrained autoencoder.•A physics-constrained loss function is employed to enhance model reliability and interpretability…”
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Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset by Sharmila, B S, Nagapadma, Rohini

“… The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices…”
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Real-time temperature anomaly detection in vaccine refrigeration systems using deep learning on a resource-constrained microcontroller by Harrabi, Mokhtar, Hamdi, Abdelaziz, Ouni, Bouraoui, Bel Hadj Tahar, Jamel

“… Our system utilizes a semi-supervised Convolutional Autoencoder (CAE) model deployed on a resource-constrained ESP32 microcontroller…”
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Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data by Cao, Yumin, Tang, Keshuang, Sun, Jian, Ji, Yangbeibei

“…•A novel methodology for recovering day-to-day dynamic OD flow.•Fusion of CV trajectories and AVI observations.•Obtaining prior OD flows by addressing…”
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Channel-Wise Autoregressive Entropy Models for Learned Image Compression by Minnen, David, Singh, Saurabh

“… Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation…”
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Hierarchical Constrained Variational Autoencoder for interaction-sparse recommendations by Li, Nuo, Guo, Bin, Liu, Yan, Ding, Yasan, Yao, Lina, Fan, Xiaopeng, Yu, Zhiwen

“… hybrid (Variational Autoencoder) VAE method reports the optimal performance with the advantages of non-linear modeling and comprehensive integration…”
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A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition by Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Liu, Mingyang, Xiao, Dengyu, Sun, Hao, Liu, Chengliang

“…•A machine parameter selection scheme for the shield machine is put forward.•A novel semi-supervised framework for recognizing geological conditions is…”
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An unsupervised region of interest extraction model for tau PET images and its application in the diagnosis of Alzheimer's disease by Shi, Rong, Wang, Luyao, Jiang, Jiehui

“… In this study, we proposed a novel deep learning model; called generative adversarial networks constrained multiple loss autoencoder for tau (GANCMLAE4TAU…”
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Constrained generative model applied to face detection by Feraud, Raphael, Bernier, Olivier, Collobert, Daniel

“…A generative neural network model, constrained by non-face examples chosen by an iterative algorithm, is applied to face detection…”
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Industrial Process Soft Sensing Based on Bidirectional Optimization Learning of Data Augmentation and Prediction Models Under Limited Data by Li, He, Wang, Zhaojing, Li, Li, Yan, Xiaoyun, Hu, Xinrong, Li, Lijun

“…). Considering that the generated samples must adhere to specific distribution characteristics and maintain the relationship between feature and target variables, a regression-constrained autoencoder (R-CAE…”
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Parametric generative schemes with geometric constraints for encoding and synthesizing airfoils by Xie, Hairun, Wang, Jing, Zhang, Miao

“… Soft-constrained scheme: a Conditional Variational Autoencoder-based model that directly incorporates geometric constraints as part of the network. 2…”
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Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations by Li, Yahang, Wang, Zepeng, Sun, Ruoyu, Lam, Fan

“… Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders…”
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