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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Deep learning advancements have spurred rapid development in AI applications.
  • Image art style transfer is a growing area of interest within deep learning.

Purpose of the Study:

  • To develop a rapid image art style transfer algorithm using generative adversarial networks (GANs).
  • To enhance the effectiveness of image artistic style transfer through novel techniques.

Main Methods:

  • Utilized a generative adversarial network (GAN) architecture.
  • Employed content and style encoders to extract image features.
  • Implemented a multi-scale discriminator to improve style transfer effects.
  • Modified deconvolution operations by adjusting image size before convolution.

Main Results:

  • The proposed algorithm effectively performs image art style transfer.
  • Experimental results validate the algorithm's efficacy and practical value.

Conclusions:

  • The developed GAN-based algorithm offers a promising solution for quick and effective image art style transfer.
  • The technique holds significant potential for broader application and promotion in the field.