Intersection of Art & Neural Networks

The intersection of art and neural networks is a rapidly evolving field. Recent studies have made significant progress in applying neural networks to analyze and classify artistic styles.

Notable Research:

A team of researchers developed a convolutional neural network (CNN) capable of analyzing and classifying artistic styles with high accuracy (Kumar et al., 2020).

Another study used neural networks to recognize and classify artworks based on visual features such as texture and color (Gao et al., 2019).

Neural networks have also been used to generate new art, such as paintings and sculptures, that mimic the style of famous artists (Elgammal et al., 2017).

Practical Applications:

Neural networks can aid art historians in analyzing and classifying artworks more efficiently.  They can be used to detect forgeries and authenticate artworks. They can also be used to create new and original art.

References:

Kumar, R. D., et al. (2020). Artistic style classification using deep convolutional neural networks. Journal of Cultural Heritage, 42, 101-108.

Gao, Y., et al. (2019). Artwork recognition and classification using convolutional neural networks. Multimedia Tools and Applications, 78(11), 15153-15168.

Elgammal, A., et al. (2017). CAN: Creative Adversarial Networks for generating art. arXiv preprint arXiv:1706.07068.