Suchergebnisse - Attention based Convolution Autoencoder

A hybrid deep learning-based fruit classification using attention model and convolution autoencoder von Xue, Gang, Liu, Shifeng, Ma, Yicao

“… chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN …”
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Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder von Liu, Jingwen, Huang, Yuchen, Wu, Dizhi, Yang, Yuchen, Chen, Yanru, Chen, Liangyin, Zhang, Yuanyuan

“… ; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE …”
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Attention Based Convolution Autoencoder for Dimensionality Reduction in Hyperspectral Images von Pande, Shivam, Banerjee, Biplab

“… To resolve these issues, deep learning techniques, such as convolution neural networks (CNNs) based autoencoders, are used …”
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GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis von Lee, Daehee, Choo, Hyunseung, Jeong, Jongpil

“… The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency …”
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Convolutional Transformer-Inspired Autoencoder for Hyperspectral Anomaly Detection von He, Zhi, He, Dan, Xiao, Man, Lou, Anjun, Lai, Guanglin

“… In this letter, a convolutional transformer-inspired autoencoder (CTA) is proposed for HAD. The CTA consists of a clustering-based module and an autoencoder-based module …”
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Robust Spatial-Temporal Autoencoder for Unsupervised Anomaly Detection of Unmanned Aerial Vehicle With Flight Data von Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli

“… Specifically, we designed a new robust spatial-temporal autoencoder (RSTAE) model based on the temporal convolution network (TCN …”
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Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering von Chen, Chao, Lu, Hu, Hong, Haotian, Wang, Hai, Wan, Shaohua

“… To solve this problem, we propose a Deep Self-Supervised Attention Convolution Autoencoder Graph Clustering (DSAGC …”
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Compact Image Transformer Based on Convolutional Variational Autoencoder with Augmented Attention Backbone for Target Recognition in Infrared Images von Nebili, Billel, Khellal, Atmane, Nemra, Abdelkrim, Boulahia, Said Yacine, Mascarilla, Laurent

“… In this direction, we proposed a Compact image Transformer based on convolutional variational Autoencoder with Augmented attention backbone (referred to AA-CiT …”
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Attention-Based Convolutional Denoising Autoencoder for Two-Lead ECG Denoising and Arrhythmia Classification von Singh, Prateek, Sharma, Ambalika

“… To achieve this, a novel attention-based convolutional denoising autoencoder (ACDAE) model is proposed that utilizes a skip-layer and attention module for reliable reconstruction of ECG signals from extreme noise conditions …”
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ADAMAEX—Alzheimer’s disease classification via attention-enhanced autoencoders and XAI von Bootun, Doorgeshwaree, Auzine, Muhammad Muzzammil, Ayesha, Noor, Idris, Salma, Saba, Tanzila, Heenaye-Mamode Khan, Maleika

“… on a convolutional autoencoder with four convolutions in the encoder part and a Squeeze and Excitation block for channel attention applied …”
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Anomaly detection for real-world electric vehicle charging data using a convolutional autoencoder with multiscale convolution, attention mechanism, and BiLSTM von Liu, Zhibin, Li, Lei, Ding, Xiaoyin, Wang, Xia, Liu, Zhiheng, Wang, Yawen, Hu, Changpeng

“… To improve the detection of anomalies in complex EV charging data, this study proposes a method based on an enhanced convolutional autoencoder (CoAE), MA-BiLSTM-MCoAE …”
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Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition von Ng, Wing, Zhang, Mingyang, Wang, Ting

“… In this work, we propose the Multi-Localized Sensitive Autoencoder-Attention-LSTM (Multi-LiSAAL) for SAR. The Localized Stochastic Sensitive Autoencoder …”
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Graph attention autoencoder inspired CNN based brain tumor classification using MRI von Mishra, Lalita, Verma, Shekhar

“… •We propose GATE-CNN architecture for binary classification of brain tumor using MR (Magnetic Resonance) images.•We have implemented our model on three …”
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Refined Multi-Focus image fusion using Multi-Scale neural network with SpSwin Autoencoder-based matting von Jiang, Shengchuan, Yu, Shanchuan

“… ) Autoencoder-based matting for the multi-focus image fusion (MFIF) task. The proposed strategy introduces several innovations to enhance image quality and address challenges like boundary precision and texture preservation …”
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Modeling Task fMRI Data Via Deep Convolutional Autoencoder von Huang, Heng, Hu, Xintao, Zhao, Yu, Makkie, Milad, Dong, Qinglin, Zhao, Shijie, Guo, Lei, Liu, Tianming

“… Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance …”
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MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection von Chen, Yurong, Zhang, Hui, Wang, Yaonan, Yang, Yimin, Zhou, Xianen, Wu, Q. M. Jonathan

“… To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system …”
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Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder von Chen, Meng, Li, Yongjian, Zhang, Liting, Liu, Lei, Han, Baokun, Shi, Wenzhuo, Wei, Shoushui

“… To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study …”
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A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals von Ghonchi, Hamidreza, Abolghasemi, Vahid

“… To address this problem, we propose a novel convolutional autoencoder (AE) network architecture to learn and extract the FECG patterns …”
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Attention-Empowered Residual Autoencoder for End-to-End Communication Systems von Lu, Min, Zhou, Bin, Bu, Zhiyong

“… Convolutional neural network (CNN)-based autoencoder can accept arbitrary lengths and is widely adopted …”
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Background-Guided Deformable Convolutional Autoencoder for Hyperspectral Anomaly Detection von Wu, Zhaoyue, Paoletti, Mercedes E., Su, Hongjun, Tao, Xuanwen, Han, Lirong, Haut, Juan M., Plaza, Antonio

“… Autoencoder (AE)-based hyperspectral anomaly detectors have received significant attention …”
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