Výsledky vyhledávání - conditioning variational autoencoder(cvae)*

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  1. 1

    CSCVAE-NID: A Conditionally Symmetric Two-Stage CVAE Framework with Cost-Sensitive Learning for Imbalanced Network Intrusion Detection Autor Wang, Zhenyu, Yu, Xuejun

    ISSN: 1099-4300, 1099-4300
    Vydáno: Switzerland MDPI AG 22.10.2025
    Vydáno v Entropy (Basel, Switzerland) (22.10.2025)
    “…: Firstly, a Data Augmentation Conditional Variational Autoencoder (DA-CVAE) is introduced…”
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    Journal Article
  2. 2

    조건부 생성모델을 이용한 강수 패턴에 따른 지하수위 생성 및 이의 활용에 관한 연구 Autor 정지호(Jiho Jeong), 정진아(Jina Jeong), 이병선(Byung Sun Lee), 송성호(Sung-Ho Song)

    ISSN: 1225-7281, 2288-7962
    Vydáno: Korea Society Of Economic&Environmental Geology 28.02.2021
    Vydáno v 자원환경지질 (28.02.2021)
    “… of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed…”
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    Journal Article
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    Class-Conditioned Variational Autoencoder with Evolutionary Optimization for the Virtual Data Generation Challenge Autor Tachioka, Yuuki

    ISSN: 2759-2871
    Vydáno: Care XDX Center, Kyushu Institute of Technology 22.05.2025
    “… To address this issue, we propose a novel data augmentation framework using conditional variational autoencoders (CVAE…”
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    Journal Article
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    Fusion U and V: Efficient MRI Sampling via the Fusion of U-net and Conditional Variational Autoencoder Autor Pershina, Khristina, Huang, Ching Chun

    ISSN: 2575-8284
    Vydáno: IEEE 09.07.2024
    “… Our paper introduces a novel approach, Fusion UandV, which employs a Conditional Variational Autoencoder (cVAE…”
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    Konferenční příspěvek
  7. 7

    Learning Conditional Variational Autoencoders with Missing Covariates Autor Ramchandran, Siddharth, Tikhonov, Gleb, Lönnroth, Otto, Tiikkainen, Pekka, Lähdesmäki, Harri

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 02.03.2022
    Vydáno v arXiv.org (02.03.2022)
    “…Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates…”
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