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EmbraceNet: A robust deep learning architecture for multimodal classification
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DOI：10.1016/j.inffus.2019.02.010.png)
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摘要
En 中文
Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning models, deals with cross-modal information carefully, and prevents performance degradation due to partial absence of data. We employ two datasets for multimodal classification tasks, build models based on our architecture and other state-of-the-art models, and analyze their performance on various situations. The results show that our architecture outperforms the other multimodal fusion architectures when some parts of data are not available.
Keyword:
Multimodal data fusion
deep learning
classification
data loss
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期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
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