arXiv cs.CY· Amalia Foka·· 12 小时前AI 评分18
Forging Emotions：用 GAN 生成视觉情感的深度学习实验
Forging Emotions: a deep learning experiment on emotions and art
AI 导读
深度学习实验 Forging Emotions 探索用视觉情感数据集和 GAN 的工作流程生成能传达或触发特定情感的图像。实验结论认为，计算机科学研究者目前构建情感这类高层概念图像数据集的方法并不充分，建议依据心理学研究利用情感联想网络。
正文 · 原文
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.
| Comments: | 10 pages. Published in Artnodes no. 31 (2023), Universitat Oberta de Catalunya, under CC BY 4.0. Extended version of a paper presented at ISEA 2022, Barcelona |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| ACM classes: | I.2.6; I.4.9; J.5 |
| Cite as: | arXiv:2610.32163 [cs.CV] |
| (or arXiv:2610.32163v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32163 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Artnodes 31 (2023) 1-10 |
| Related DOI: | https://doi.org/10.7238/artnodes.v0i31.402397
DOI(s) linking to related resources |
Submission history
From: Amalia Foka [view email]
[v1]
Fri, 23 Oct 2026 04:54:00 UTC (1,686 KB)
来源：arXiv cs.CY · arxiv.org