A flow model with a more-added coupling function

Xiang Ji; Xiaoming Ling; Hongyan Chen; Xiaoyu Zhang; Zefei Dang

doi:10.1117/12.3012278

21 December 2023 A flow model with a more-added coupling function

Xiang Ji, Xiaoming Ling, Hongyan Chen, Xiaoyu Zhang, Zefei Dang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297035 (2023) https://doi.org/10.1117/12.3012278
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Unsupervised learning of probabilistic models is a core problem in machine learning. Under the foundation that the model of easy data modeling is good, the NICE model is proposed. A deep learning framework for complex high-dimensional density modeling is presented in NICE. In this paper, CovnFlow is added to NICE to construct a new coupled flow model. Although the basic model of NICE has been changed, the calculation of the Jacobi matrix is not much more complicated, and the newly proposed model will allow for a fuller fusion of information. So using this model to generate pictures will be more efficient and the effect will be better.

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

Citation Download Citation

Xiang Ji, Xiaoming Ling, Hongyan Chen, Xiaoyu Zhang, and Zefei Dang "A flow model with a more-added coupling function", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297035 (21 December 2023); https://doi.org/10.1117/12.3012278

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KEYWORDS

Matrices

Data modeling

Convolution

Machine learning

Neural networks

Information fusion

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