2020
DOI: 10.1098/rsos.191569
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Modelling and forecasting art movements with CGANs

Abstract: Conditional Generative Adversarial Networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions f (1) , . . . , f (K) . This training allows CGANs to generate samples from a sequence of distributions. We apply our method to pain… Show more

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Cited by 3 publications
(3 citation statements)
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References 17 publications
(40 reference statements)
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“…Since the 2010s, there has been an explosion in the number of papers exploring art forms using computational methods ([ 5 , 6 , 7 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]). This has been due to the more formal organisation of image-based datasets, and the large-scale use of neural-network- and data-science-based approaches for image analysis.…”
Section: Computation and Artmentioning
confidence: 99%
See 1 more Smart Citation
“…Since the 2010s, there has been an explosion in the number of papers exploring art forms using computational methods ([ 5 , 6 , 7 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]). This has been due to the more formal organisation of image-based datasets, and the large-scale use of neural-network- and data-science-based approaches for image analysis.…”
Section: Computation and Artmentioning
confidence: 99%
“…Another approach is to use generative adversarial networks [ 10 ], which are neural networks trained to generate images that mimic art styles or genres. Such models can be used in tandem with temporal data to predict future art movements and styles [ 23 ].…”
Section: Computation and Artmentioning
confidence: 99%
“…Among them, painting is an important part of art history and an object of study for computational aesthetics in the visual field. Quantitative studies of paintings can not only provide auxiliary information for the appreciation of art [ 8 , 9 , 10 ] but also enable machines to learn human perceptual behaviors for imitative creation [ 11 , 12 ].…”
Section: Introductionmentioning
confidence: 99%