模仿绘画风格的算法:A Neural Algorithm of Artistic Style
2016-07-27 22:12
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Neural Algorithm of Artistic Style 将名画的风格放在写实照上---神奇的算法
GitHub地址:https://github.com/kaishengtai/neuralart
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay
between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human
performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses
neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks
and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
Neural Algorithm of Artistic Style 将名画的风格放在写实照上---神奇的算法
GitHub地址:https://github.com/kaishengtai/neuralart
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay
between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human
performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses
neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks
and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
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