洛可可绘画名作风格迁移的深度学习

K. Kim, Dohyun Kim, Joongheon Kim
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引用次数: 2

摘要

本文考虑了通过风格转移对原始绘画图像应用的一般适应。实验结果表明,以往使用预训练CNN的风格迁移研究和使用GAN的风格迁移研究只是算法或结构不同,但问题是相同的。这是各种绘画风格的非一般应用。洛可可画风实验结果与印象派画风实验结果的显著差异说明了上述问题。特别是洛可可绘画风格中风格迁移方法应用的尴尬结果的推导就代表了这类问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hardness on Style Transfer Deep Learning for Rococo Painting Masterpieces
This paper considers the general adaptation of the application of raw painting images via style transfer. Experimental results show that both the previous studies style transfer using pre-trained CNN and style transfer using GAN has only different algorithms or structure but same problem. That is the un-general application in various painting styles. A striking difference between experiment results in Rococo painting style and experiment results in Impressionism painting style speak for the above problem. In particular, the derivation of awkward results for the application of style transfer method in Rococo painting style represents this kind of problem.
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