How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network

@article{Zahedi2022HowDI,
  title={How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network},
  author={Mahan Agha Zahedi and Niloofar Gholamrezaei and Alex Doboli},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.16031}
}
Mathematical modeling and aesthetic rule extraction of works of art are complex activities. This is because art is a multidimensional, subjective discipline. Perception and interpretation of art are, to many extents, relative and open-ended rather than measurable. Following the explainable Artificial Intelligence paradigm, this paper investigated in a human-understandable fashion the limits to which a single-task, single-modality benchmark computer vision model performs in classifying… 

Inching Towards Automated Understanding of the Meaning of Art: An Application to Computational Analysis of Mondrian's Artwork

  • Alex DoboliMahan Agha ZahediNiloofar Gholamrezaei
  • Art
  • 2022
. Deep Neural Networks (DNNs) have been successfully used in classifying digital images but have been less succesful in classifying images with meanings that are not linear combinations of their

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