Abstract
The essence of image classification task is to extract high-level semantic content features of images. The traditional data enhancement methods based on convolutional neural network (CNN) are translation, rotation, clipping, noise adding, etc. These methods have not changed the content and style of image data. This paper proposes a fast style migration data enhancement method, which can quickly apply the style art of one image to another image without changing the high-level semantic content characteristics of the image. Through the experimental comparison, it is found that the method of fast style migration data enhancement proposed here can further improve the accuracy of the model compared with the traditional data.
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Acknowledgements
This work has been partially supported by “Heilongjiang Science Foundation Project (LH2021F052)” and “2020 scientific research project of basic scientific research expenses of provincial colleges and universities in Heilongjiang Province (2020-KYYWF-0684)”.
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Zheng, S., Wu, J., Liu, F., Pan, J., Qiao, Z. (2022). Research on Image Binary Classification Based on Fast Style Transfer Data Enhancement. In: Jiang, X. (eds) Machine Learning and Intelligent Communications. MLICOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-04409-0_8
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DOI: https://doi.org/10.1007/978-3-031-04409-0_8
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