Difference between revisions of "Classification and Generation of Digital Marble Art: OR and Deep Learning"
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- our results are partially answering recent open questions about GAN framework proposed by (Odena [2019]). | - our results are partially answering recent open questions about GAN framework proposed by (Odena [2019]). | ||
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link to material: https://ifors.org/wp-content/uploads/2019/11/Willi-Meltem-Suryati-EBRU-North-Sumatra-November-2019.pdf | link to material: https://ifors.org/wp-content/uploads/2019/11/Willi-Meltem-Suryati-EBRU-North-Sumatra-November-2019.pdf | ||
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[[Category:General Articles]] | [[Category:General Articles]] |
Revision as of 03:54, 11 November 2019
by: Gerhard Wilhelm Weber, Meltem Atay & Suryati Sitepu
OR and Art
- Generative adversarial networks (GANs) are types of neural networks which can be used to generate new data.
- they can be used to generate art pieces.
- We developed a new GAN framework specific to generate digital abstract art,
- we are introducing an open digital dataset for further research and collaboration opportunities.
Novelty
- This idea is based on merging certain Support Vector Machines (SVMs) (invented by Drucker et al. [1997]) with convolution operation of Convolutional Neural Networks (CNNs) (proposed by LeCun et al. [1995]),
- kernels widely used for SVMs are linear, nonlinear and radial basis function (RBF) kernel methods tested for CNNs,
- we are proposing a general model which would be able to generate visually attractive art pieces without human supervision using OR methodology (Sack [2019]),
- our results are partially answering recent open questions about GAN framework proposed by (Odena [2019]).
link to material: https://ifors.org/wp-content/uploads/2019/11/Willi-Meltem-Suryati-EBRU-North-Sumatra-November-2019.pdf