Search Results - "Deep supervised autoencoder"

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  1. 1

    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

    ISSN: 1545-5963, 1557-9964, 1557-9964
    Published: New York IEEE 01.09.2022
    “… 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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    Journal Article
  2. 2

    Captured multi-label relations via joint deep supervised autoencoder by Lian, Si-ming, Liu, Jian-wei, Lu, Run-kun, Luo, Xiong-lin

    ISSN: 1568-4946, 1872-9681
    Published: Elsevier B.V 01.01.2019
    Published in Applied soft computing (01.01.2019)
    “… 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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    Journal Article
  3. 3

    Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition by Zhou, Jiliu, Li, Guoqi, Liu, Chang, Huang, Rongbing

    ISSN: 1024-123X, 1563-5147
    Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
    Published in Mathematical problems in engineering (01.01.2016)
    “…Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition…”
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    Journal Article
  4. 4

    DeepPos: Deep Supervised Autoencoder Network for CSI Based Indoor Localization by Yazdanian, Peyman, Pourahmadi, Vahid

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 27.11.2018
    Published in arXiv.org (27.11.2018)
    “…The widespread mobile devices facilitated the emergence of many new applications and services. Among them are location-based services (LBS) that provide…”
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    Paper
  5. 5

    Single Sample Face Recognition via Learning Deep Supervised Autoencoders by Gao, Shenghua, Zhang, Yuting, Jia, Kui, Lu, Jiwen, Zhang, Yingying

    ISSN: 1556-6013, 1556-6021
    Published: New York IEEE 01.10.2015
    “…This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in…”
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    Journal Article
  6. 6

    Pulmonary nodule diagnosis using dual‐modal supervised autoencoder based on extreme learning machine by Qiang, Yan, Ge, Lei, Zhao, Xin, Zhang, Xiaolong, Tang, Xiaoxian

    ISSN: 0266-4720, 1468-0394
    Published: Oxford Blackwell Publishing Ltd 01.12.2017
    Published in Expert systems (01.12.2017)
    “…‐modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data…”
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    Journal Article
  7. 7

    Asymmetric Supervised Deep Autoencoder for Depth Image based 3D Model Retrieval by Siddiqua, Ayesha, Fan, Guoliang

    ISSN: 2642-9357
    Published: IEEE 01.12.2019
    “… The proposed asymmetric deep supervised autoencoder deals with the incompleteness and ambiguity present in the depth images by balancing reconstruction and classification capabilities in a unified…”
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    Conference Proceeding
  8. 8

    Automatic Dialect Detection for Low Resource Santali Language by Sahoo, Sunil Kumar, Mishra, Brojo Kishore, Parida, Shantipriya, Dash, Satya Ranjan, Besra, Jatindra Nath, Tello, Esau Villatoro

    Published: IEEE 01.12.2021
    “… We considered the Sanatali written in Odia script and standard Odia script. We used a deep supervised autoencoder for language detection…”
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    Conference Proceeding
  9. 9

    Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions by Villatoro-Tello, Esaú, Parida, Shantipriya, Kumar, Sajit, Motlicek, Petr

    ISSN: 1866-9956, 1866-9964
    Published: New York Springer US 01.09.2021
    Published in Cognitive computation (01.09.2021)
    “… We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep Supervised Autoencoders for solving the OMT task…”
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    Journal Article