Deep learning fusion model and reliability analysis

Nishui Cai; Yiang Gao

doi:10.1117/12.3009536

16 October 2023 Deep learning fusion model and reliability analysis

Nishui Cai, Yiang Gao

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030J (2023) https://doi.org/10.1117/12.3009536
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

With the widespread use of big data and artificial intelligence technologies, the complexity of prediction problems such as classification, clustering, and regression is increasing, and the requirements for prediction models generally call for the fusion of different individual learners to achieve these goals. Although the prediction accuracy of the new fusion model can be improved to some extent by model fusion, the structure of the fusion model is more complex, the computationally intensive prediction time increases, and the reliability suffers. In this paper, we firstly systematically sort out the deep learning model fusion methods, secondly, analyze the coupling types of different model fusion methods and the impact on the reliability of the prediction system, and finally construct a fusion model for false information multimodal detection using model fusion methods for the needs of false information multimodal detection application scenarios and analyze its reliability impact.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nishui Cai and Yiang Gao "Deep learning fusion model and reliability analysis", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030J (16 October 2023); https://doi.org/10.1117/12.3009536

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