Generative Adversarial Networks for Down Syndrome Face Generation

This paper presents a method for generating realistic images of individuals with Down Syndrome using the state-of-the-art Generative Adversarial Network (GAN) architecture StyleGAN2. The goal of this work is to create a set of images that can be used to evaluate the performance of image recognition algorithms on individuals with Down Syndrome. To achieve this goal, we collected a dataset of real-world images of individuals with Down syndrome manually from the internet. The dataset was then preprocessed and used to train and evaluate our GAN model. We experimented with different configurations of StyleGAN2. We trained and evaluated the GAN using different dataset sizes, ranging from 400 to 1000 images, to investigate the effect of dataset size on the performance of the model. The results indicate that using a larger dataset size improves the overall quality and diversity of the generated images. Our experiments with StyleGAN2 demonstrated the ability of the model to generate high-quality, diverse images that can be used to evaluate the performance of image recognition algorithms on individuals with Down Syndrome.