Forging Emotions: a deep learning experiment on emotions and art

IF 0.2 0 HUMANITIES, MULTIDISCIPLINARY
Amalia F. Foka
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引用次数: 1

Abstract

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 focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by 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, that is, for generating 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, such as bright or dark colours, does not seem adequate. Therefore, research efforts should aim to understand 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.
锻造情感:关于情感和艺术的深度学习实验
情感计算是一个跨学科领域,研究与情感相关或影响情感的计算方法。这些方法已经应用于互动媒体艺术作品中,但它们侧重于情感检测而不是情感生成。对于情感生成,需要探索最近由生成对抗网络(GANs)驱动的计算创造性方法,这是一种深度学习方法。本文中提出的实验“锻造情感”(Forging Emotions)探讨了视觉情感数据集的使用以及gan在视觉情感生成中的工作过程,即生成能够传达或触发特定情感的图像。本实验得出的结论是,迄今为止计算机科学研究人员用于构建描述高级概念(如情绪)的图像数据集的方法是不够的,并建议根据心理学研究利用情感关联网络。锻造情感还得出结论,要在视觉上产生情感,仅仅与基本的心理学发现相对应,比如明亮或黑暗的颜色,似乎是不够的。因此,研究工作的目标应该是理解训练gan和合成gan的结构,以产生真正新颖的合成,通过生成的图像的主题来传达或触发情感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artnodes
Artnodes HUMANITIES, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
26
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