  Experiments in affective computing are based on stimulus datasets that, in
the process of standardization, receive metadata describing which emotions each
stimulus evokes. In this paper, we explore an approach to creating stimulus
datasets for affective computing using generative adversarial networks (GANs).
Traditional dataset preparation methods are costly and time consuming,
prompting our investigation of alternatives. We conducted experiments with
various GAN architectures, including Deep Convolutional GAN, Conditional GAN,
Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN,
alongside data augmentation and transfer learning techniques. Our findings
highlight promising advances in the generation of emotionally evocative
synthetic images, suggesting significant potential for future research and
improvements in this domain.
