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Artificial Neural Networks and Deep Learning in Visual Arts: a Review

Authors: Santos, Iria; Castro, M. Luz; Rodríguez-Fernández, Nereida; Torrente-Patiño, Álvaro; Carballal, Adrián;

Artificial Neural Networks and Deep Learning in Visual Arts: a Review

Abstract

[Abstract]: In this article we make an intensive analysis of the use of Artificial Neural Network and Deep Learning in Visual Arts. We introduce the content on Artificial Intelligence over the years and examine in depth the latest work carried out in prediction, classi_cation, evaluation, generation, and identification through Artifiial Neural Networks for the different Visual Arts. We highlight the contributions of photography and pictorial artworks, but there are also other uses for 3D modeling, video games, architecture, or comics. The reported results of the different investigations mentioned show us that, in the field of Visual Arts, Artificial Neural Networks continue to evolve constantly and that lately they show significant growth. To complement the text, we include a table with information about the most employed image data sets and a glossary.

Country
Spain
Related Organizations
Keywords

Generative Adversarial Networks, Identification, Convolutional Neural Networks, Generation, Transfer Learning, Classification, Machine Learning, Deep Learning, Datasets, Visual Arts, Prediction, Evaluation, Artificial Neural Networks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green