Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods
This addresses dataset preparation challenges for affective computing researchers, though it appears incremental as it builds on existing GAN architectures.
The paper tackles the problem of costly and time-consuming stimulus dataset creation for affective computing by exploring GAN-based approaches to generate emotionally evocative synthetic images, reporting promising advances in this domain.
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.