A survey of multimodal deep generative models

被引：0
|
作者：
Suzuki, Masahiro [1 ]
Matsuo, Yutaka [1 ]
机构：
[1] Department of Technology Management for Innovation, The University of Tokyo, Tokyo, Japan
来源：
Advanced Robotics | 2022年 / 36卷 / 5-6期
关键词：
Auto encoders - Building model - Cross-modal - Deep generative model - Generative model - Multi-modal - Multi-modal data - Multi-modal learning - Prediction-based - Shared representations;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
126
引用
收藏
页码：261 / 278
相关论文
共 50 条
  • [21] Learning Intuitive Physics with Multimodal Generative Models
    Rezaei-Shoshtari, Sahand
    Hogan, Francois R.
    Jenkin, Michael
    Meger, David
    Dudek, Gregory
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 6110 - 6118
  • [22] Asymmetric deep generative models
    Partaourides, Harris
    Chatzis, Sotirios P.
    NEUROCOMPUTING, 2017, 241 : 90 - 96
  • [23] Auxiliary Deep Generative Models
    Maaloe, Lars
    Sonderby, Casper Kaae
    Sonderby, Soren Kaae
    Winther, Ole
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 48, 2016, 48
  • [24] Deep generative models in DataSHIELD
    Lenz, Stefan
    Hess, Moritz
    Binder, Harald
    BMC MEDICAL RESEARCH METHODOLOGY, 2021, 21 (01)
  • [25] An Overview of Deep Generative Models
    Xu, Jungang
    Li, Hui
    Zhou, Shilong
    IETE TECHNICAL REVIEW, 2015, 32 (02) : 131 - 139
  • [26] Denoising Deep Generative Models
    Loaiza-Ganem, Gabriel
    Ross, Brendan Leigh
    Wu, Luhuan
    Cunningham, John P.
    Cresswell, Jesse C.
    Caterini, Anthony L.
    PROCEEDINGS ON I CAN'T BELIEVE IT'S NOT BETTER! - UNDERSTANDING DEEP LEARNING THROUGH EMPIRICAL FALSIFICATION, VOL 187, 2022, 187 : 41 - 50
  • [27] A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
    Suh, Namjoon
    Cheng, Guang
    ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 2025, 12 : 177 - 207
  • [28] Emergent communication of multimodal deep generative models based on Metropolis-Hastings naming game
    Hoang, Nguyen Le
    Taniguchi, Tadahiro
    Hagiwara, Yoshinobu
    Taniguchi, Akira
    FRONTIERS IN ROBOTICS AND AI, 2024, 10
  • [29] MultiVI: deep generative model for the integration of multimodal data
    Tal Ashuach
    Mariano I. Gabitto
    Rohan V. Koodli
    Giuseppe-Antonio Saldi
    Michael I. Jordan
    Nir Yosef
    Nature Methods, 2023, 20 : 1222 - 1231
  • [30] MultiVI: deep generative model for the integration of multimodal data
    Ashuach, Tal
    Gabitto, Mariano I.
    Koodli, Rohan V.
    Saldi, Giuseppe-Antonio
    Jordan, Michael I.
    Yosef, Nir
    NATURE METHODS, 2023, 20 (08) : 1222 - +
12345