Search Results - Deep supervised autoencoder

Semi‐supervised deep autoencoder for seismic facies classification by Liu, Xingye, Li, Bin, Li, Jingye, Chen, Xiaohong, Li, Qingchun, Chen, Yangkang

“…‐supervised deep autoencoder by taking the mean of intra‐class and the whole population of facies into account to construct a classification regularization term, thereby improving the classification accuracy…”
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Single Sample Face Recognition via Learning Deep Supervised Autoencoders by Gao, Shenghua, Zhang, Yuting, Jia, Kui, Lu, Jiwen, Zhang, Yingying

“… Motivated by the success of deep learning in image representation, we propose a supervised autoencoder, which is a new type of building block for deep architectures…”
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Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images by Cheng, Jianhong, Zhao, Wei, Liu, Jin, Xie, Xingzhi, Wu, Shangjie, Liu, Liangliang, Yue, Hailin, Li, Junjian, Wang, Jianxin, Liu, Jun

“… In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images…”
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Captured multi-label relations via joint deep supervised autoencoder by Lian, Si-ming, Liu, Jian-wei, Lu, Run-kun, Luo, Xiong-lin

“… Meanwhile, it is not advisable to suppose that multiple labels are independent of each other. Therefore, we propose the deep supervised autoencoder as a generative model…”
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Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval by Siddiqua, Ayesha, Fan, Guoliang

“…•We propose a new supervised autoencoder for 3D model retrieval from depth images…”
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Deep supervised multimodal semantic autoencoder for cross‐modal retrieval by Tian, Yu, Yang, Wenjing, Liu, Qingsong, Yang, Qiong

“… With semantic deep autoencoders, MMCA‐CMR promotes a more reliable…”
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Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition by Zhou, Jiliu, Li, Guoqi, Liu, Chang, Huang, Rongbing

“…Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR…”
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R/C buildings’ seismic damage prediction based on semi-supervised automatic differentiation variational inference deep autoencoder by Demertzis, K, Kostinakis, K, Morfidis, K, Iliadis, L

“…Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML)…”
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Semi-Supervised Deep Conditional Variational Autoencoder for Soft Sensor Modeling by Tang, Xiaochu, Yan, Jiawei, Li, Yuan, Zhang, Xinmin, Song, Zhihuan

“…Variational autoencoder (VAE) as an unsupervised deep generated model has been widely applied to process modeling for industrial processes due to its excellent ability in nonlinear and uncertain feature extraction…”
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Just-in-time updating soft sensor model of endpoint carbon content and temperature in BOF steelmaking based on deep residual supervised autoencoder by Yang, Lu, Liu, Hui, Chen, Fugang

“… Thus, this paper proposes a feature extraction model based on deep residual supervised autoencoder (DRSupAE…”
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A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine by Ezzati Khatab, Zahra, Hajihoseini Gazestani, Amirhosein, Ghorashi, Seyed Ali, Ghavami, Mohammad

“…•Using unlabeled data to encourage more participant in crowd-sensing process.•Decreasing the complexity and improving the accuracy.•Real test in addition to…”
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SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual Autoencoder by Berahmand, Kamal, Bahadori, Sondos, Abadeh, Maryam Nooraei, Li, Yuefeng, Xu, Yue

“… To address these limitations, we propose a novel method called Semi-supervised Deep Attributed Clustering using Dual Autoencoder (SDAC-DA…”
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Asymmetric Supervised Deep Autoencoder for Depth Image based 3D Model Retrieval by Siddiqua, Ayesha, Fan, Guoliang

“…In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images…”
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Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering by 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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Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario by Khajehrayeni, Farshid, Ghassemian, Hassan

“… This article introduces two approaches intending to solve the challenge of the mixed pixels using deep convolutional autoencoders (DCAEs…”
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Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection by Yuan, Chengsheng, Chen, Xianyi, Yu, Peipeng, Meng, Ruohan, Cheng, Weijin, Wu, Q. M. Jonathan, Sun, Xingming

“…The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are…”
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A novel deep quality-supervised regularized autoencoder model for quality-relevant fault detection by Li, Zhichao, Tian, Li, Yan, Xuefeng

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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition by 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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Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering by Wang, Yang, Xiao, Hong, Zhang, Zhihai, Guo, Xiaoxuan, Liu, Qiang

“… Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key…”
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Self-Supervised Deep Hadamard Autoencoders for Treating Missing Data: A Case Study in Manufacturing by Karkare, Rasika, Paffenroth, Randy, Apelian, Diran

“…Data collected from sensors are pivotal to the Industrial Internet of Things (IIoT) applications as they would not be usable if the data quality is bad…”
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