  Generating preferred images using generative adversarial networks (GANs) is
challenging owing to the high-dimensional nature of latent space. In this
study, we propose a novel approach that uses simple user-swipe interactions to
generate preferred images for users. To effectively explore the latent space
with only swipe interactions, we apply principal component analysis to the
latent space of the StyleGAN, creating meaningful subspaces. We use a
multi-armed bandit algorithm to decide the dimensions to explore, focusing on
the preferences of the user. Experiments show that our method is more efficient
in generating preferred images than the baseline methods. Furthermore, changes
in preferred images during image generation or the display of entirely
different image styles were observed to provide new inspirations, subsequently
altering user preferences. This highlights the dynamic nature of user
preferences, which our proposed approach recognizes and enhances.
