Human Evaluators Fooled By Princeton Student's AI Generated Chinese Paintings

  • December 01, 2020 15:52

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Chinese landscape paintings created by (a) human artists, (b) baseline models (top painting from RaLSGAN [9], bottom painting from StyleGAN2 [13]), and two GANs, (c) and (d), within proposed Sketch-And-Paint framework.
Alice Xue

A college student's thesis work has made strides in machine-original art generation — at least in artistic styles that emphasize edge definition.

The student created a data set of over 2,000 traditional Chinese landscape paintings from museum collections to train the model. 

Synched reports, "Princeton undergrad student Alice Xue has designed a GAN framework for Chinese landscape painting generation that is so effective most humans can’t distinguish its works from the real thing. ...The proposed framework, Sketch-And-Paint GAN (SAPGAN), is the first end-to-end model for Chinese landscape painting generation without conditional input. The 242 participants in a visual Turing test identified SAPGAN paintings as human artworks with a frequency significantly higher than paintings from baseline GANs."

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The works created by SAGPAN were mistaken by the human participants as human art as often as 55 percent.

Read the paper End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks on arXiv.

Read more at Synched


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