“Forging emotions. A deep learning experiment on emotions and art” presented by Foka

Symposium:

• ISEA2022: 27th International Symposium on Electronic Art
• More presentations from ISEA2022:  

• Beyond Black Box Machines

• Forging emotions. A deep learning experiment on emotions and art

• Amalia Foka

Venue(s):

• CCCB: Centre de Cultura Contemporània de Barcelona, Barcelona, Spain

• Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have been focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that lately have been driven with the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the working processes of GANs for visual affect generation, i.e., to generate images that can convey or trigger specified emotions. This experiment concludes that the methodology used so far by computer science researchers to build image datasets for describing high-level concepts such as emotions is insufficient and proposes utilizing emotional networks of associations according to psychology research. Forging Emotions also concludes that to generate affect visually, merely corresponding to basic psychology findings, e.g., bright or dark colors, does not seem adequate. Therefore, research efforts should be targeted towards understanding the structure of trained GANs and compositional GANs in order to produce genuinely novel compositions that can convey or trigger emotions through the subject matter of generated images.

Keywords:

• deep learning
• affective computing
• visual emotion datasets
• generative adversarial network(GAN)

Category:

• Long Paper
• Paper