Modelling and understanding aesthetic preferences for visual patterns, photographs and paintings: Comparing human perceivers with convolutional neural networks KU Leuven
In spite of the wide-spread belief that “beauty is in the eye of the beholder”, recent research in empirical aesthetics has focused on the role of statistical image properties as quasi-universal, biologically rooted factors underlying the preference for some patterns, photographs, and paintings. In the slipstream of the booming area of machine learning (deep neural networks, DNNs, and convolutional neural networks, CNNs), a new field has ...