Author(s)

Kaile Sun ¹

Affiliation(s)

¹ Zhengzhou Business University, Zhengzhou, 451200, China

Corresponding Author

Kaile Sun

ABSTRACT

This paper delves into the application and performance of deep learning algorithms in the field of image recognition. By comparing different deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN), we analyze the efficiency and accuracy of these models in handling various image recognition tasks. The research also includes discussions on optimization strategies for these algorithms and potential challenges and solutions in real-world applications. 

KEYWORDS

Deep learning, image recognition, Convolutional Neural Network, Recurrent Neural Network, Generative Adversarial Network, performance analysis

CITE THIS PAPER

Kaile Sun, Analysis of the performance of deep learning algorithms in image recognition. Journal of Image Processing Theory and Applications (2024) Vol. 7: 12-18. DOI: http://dx.doi.org/10.23977/jipta.2024.070102.

REFERENCES

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[3] Li Yanqiu. Image classification algorithm of the Gray-Scott model based on the convolutional neural network [J]. Journal of Applied Technology. 2023,23(04):403-409.
[4] Yue Yurong. Human pose recognition method in infrared images based on deep convolutional network [J]. Journal of Hebei Normal University (Natural Science Edition). 2023(12):9.
[5] Xue Liang. Research and application of Image Recognition algorithm based on Deep Learning [J]. Information recording materials. 2023,24(07):105-107.
[6] Hao Baocong. Establishment and completion technology of state Grid data model knowledge graph [J]. Fujian computer. 2023,39(12):1-7.

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