Suchergebnisse - Fully connected sparse autoencoder

A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction von Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li

“… In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP …”
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Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network von Yang, Pu, Wen, Chenwan, Geng, Huilin, Liu, Peng

“… autoencoder includes a fully connected autoencoding network, the features extracted from the front layer of the network …”
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Optimisation of sparse deep autoencoders for dynamic network embedding von Tang, Huimei, Zhang, Yutao, Ma, Lijia, Lin, Qiuzhen, Huang, Liping, Li, Jianqiang, Gong, Maoguo

“… SPDNE tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic NE …”
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Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging von Zeng, Hong, Dong, Jiawei, Li, Qianxi, Chen, Weining, Dong, Sen, Guo, Huinan, Wang, Hao

“… the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel …”
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Auto encoder Based Tomato Leaf Disease Identification von Saranya, S Mohana, Prabavathi, R, Brindha, V Devi, Subha, P, Mohanapriya, S, Deepa, B

“… Initially Autoencoder is used to remove the noise as an initial preprocessing step. Various autoencoders like Simple autoencoder based on fully-connected layer, sparse …”
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Patch-based Sparse and Convolutional Autoencoders for Anomaly Detection in Hyperspectral Images von Rezvanian, Amir Reza, Imani, Maryam, Ghassemian, Hassan

“… The proposed networks are deep fully-connected sparse autoencoders (SAE) and deep one-dimensional convolutional autoencoders (CAE …”
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Long-Distance Pipeline Safety Early Warning: A Distributed Optical Fiber Sensing Semi-Supervised Learning Method von Yang, Yiyuan, Zhang, Haifeng, Li, Yi

“… Concretely, the sparse stacked autoencoder trained …”
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Improved Multi-Echo Gradient-Echo-Based Myelin Water Fraction Mapping Using Dimensionality Reduction von Song, Jae Eun, Kim, Dong-Hyun

“… Specifically, we implemented a fully connected deep autoencoder to extract the low-dimensional features of complex-valued signals and incorporated a sparse regularization to separate the anomaly …”
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Research on Graphene Thermoacoustic Speakers Based on a Sparse Autoencoder-Back Propagation Neural Network Model von Li, Huabo, Wang, Debo, Chen, Jie, Liang, Huaxiang

“… To mitigate the complexities of traditional theoretical models for graphene thermoacoustic speakers, which are hampered by multifarious influencing factors …”
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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings von Zhu, Haiping, Cheng, Jiaxin, Zhang, Cong, Wu, Jun, Shao, Xinyu

“… Different from the traditional autoencoder, the proposed sPSDAE model, including a fully connected autoencoder network, uses the superior features extracted in all the previous layers to participate …”
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Perturbation of deep autoencoder weights for model compression and classification of tabular data von Abrar, Sakib, Samad, Manar D.

“… Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements …”
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DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder von Chen, Hao, Chen, Tao, Zhang, Yuxiang, Du, Bo, Plaza, Antonio

“… Hyperspectral unmixing (HU) techniques serve as effective means to address this issue. In recent years, deep learning methods, particularly autoencoders (AEs …”
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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition von Mohana, M., Subashini, P., Ghinea, George

“… as the Deep Semi-supervised Convolutional Sparse Autoencoder to address the aforementioned issues and enhance FER performance and prediction …”
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Electroencephalogram Sensor Data Compression Using an Asymmetrical Sparse Autoencoder with a Discrete Cosine Transform Layer von Zhu, Xin, Pan, Hongyi, Rong, Shuaiang, Cetin, Ahmet Enis

“… The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity …”
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Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images von Wan, Xiaoqing, Zhao, Chunhui

“… As a competitive machine learning algorithm, the stacked sparse autoencoder (SSA) has achieved outstanding popularity in exploiting high-level features for classification of hyperspectral images …”
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Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data von Liu, Xuyang, Duan, Chaoshu, Cai, Wensheng, Shao, Xueguang

“… ) was developed in this work for the separation of the mixed spectra in HSI. The proposed model is composed of an encoder and a fully connected (FC) layer …”
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Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation von Lei, Ke, Tan, Zhongsheng, Wang, Xiuying, Zhou, Zhenliang

“… Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets …”
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Deep Sparse Representation-Based Classification von Abavisani, Mahdi, Patel, Vishal M.

“… The proposed network consists of a convolutional autoencoder along with a fully connected layer …”
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GADRP: graph convolutional networks and autoencoders for cancer drug response prediction von Wang, Hong, Dai, Chong, Wen, Yuqi, Wang, Xiaoqi, Liu, Wenjuan, He, Song, Bo, Xiaochen, Peng, Shaoliang

“… ) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction …”
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Input-aware Sparse Tensor Storage Format Selection for Optimizing MTTKRP von Sun, Qingxiao, Liu, Yi, Yang, Hailong, Dun, Ming, Luan, Zhongzhi, Gan, Lin, Yang, Guangwen, Qian, Depei

“… ). To optimize the performance of MTTKRP, various sparse tensor formats have been proposed such as CSF and HiCOO …”
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