Classification and Generation of Digital Marble Art: OR and Deep Learning
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.
- This idea is based on merging certain Support Vector Machines (SVMs) (invented by Drucker et al. ) with convolution operation of Convolutional Neural Networks (CNNs) (proposed by LeCun et al. ),
- 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 ),
- our results are partially answering recent open questions about GAN framework proposed by (Odena ).