Creative art generation with a style-based architecture for generative adversarial networks
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2021-06-18
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en
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Abstract
Combining the state of the art Generative Adversarial Network (GAN) architecture
of StyleGAN2 with the artistic style ambiguity loss of Creative
Adversarial Network (CAN) has promises for improving the creative art
eld because styles are more controlled and higher resolutions can be reached
than before. Even though my implementation does not achieve the expected
results but su ers from common GAN problems, contributions are made towards
the development and evaluation of such improved creative networks,
and its societal relevance is discussed. A questionnaire for human evaluation
is developed as I argue that objective GAN metrics give ambiguous results
in the creative eld. Furthermore, this area gives contributions towards Arti
cial General Intelligence, but also additional developments towards identifying
deep fakes are required. Overall, my research together with follow
up implementation improvements help understanding the possibilities and
complexities of this intriguing eld of creative computer-generated art.
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Faculteit der Sociale Wetenschappen