A Review of Style Transfer Methods Based on Deep Learning
With the rapid development of deep learning,style transfer technology has made significant breakthroughs in algo-rithms and applications,providing strong support for innovative interaction between content and styles.This paper reviews the basic concepts,classifications and applications of style transfer in neural networks,focusing on the principles,variations and synthesis algorithms of neural style transfer.A comparison between text-based image style transfer and image-based methods was conducted,analyzed their respective advantages and disadvantages,highlighted the development of intelligent style transfer technology.Furthermore,the integration of style transfer technology with other fields was discussed,such as its combinations with super-resolution methods and contrastive learning methods,as well as its application examples in large-scale artwork de-sign,demonstrated its extensive potential applications.The purpose of this paper was providing researchers with a clear per-spective and promoting technological advancement in the field of style transfer.
neural style transferdeep learningalgorithmsperformance improvementtext-based style transfer
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