Research Area: Machine Learning
Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and cross-modal generation via these representations; however, achieving this requires taking the heterogeneous nature of multimodal data into account. In recent years, deep generative models, i.e. generative models in which distributions are parameterized by deep neural networks, have attracted much attention, especially variational autoencoders, which are suitable for accomplishing the above challenges because they can consider heterogeneity and infer good representations of data. Therefore, various multimodal generative models based on variational autoencoders, called multimodal deep generative models, have been proposed in recent years. In this paper, we provide a categorized survey of studies on multimodal deep generative models.
Keywords:
Multimodal
Deep Generative Models
deep neural networks
autoencoders
multimodal deep generative models
Deep Learning
Machine Learning
Author(s) Name: Masahiro Suzuki, Yutaka Matsuo
Journal name: Advanced Robotics
Conferrence name:
Publisher name: Taylor and Francis
DOI: https://doi.org/10.1080/01691864.2022.2035253
Volume Information:
