Modeling and simulation of noise vectors for improving diversity and quality of a generative AI

Generative Artificial Intelligence (Generative AI) models, such as the Generative Adversarial Networks (GANs), use noise vectors to access subspaces of a latent space for generating diverse and quality output. In the current GAN models, noise vectors are generally drawn from Gaussian distributions to achieve meaningful diversity and quality images. Since the focus of Generative AI is mainly on the generation of diversity and quality, it is important to understand the contributions of the noise vectors that are drawn from different statistical distributions to access the quality latent subspaces. This is currently a gap in the Generative AI research. This paper presents a learning-based modeling and simulation framework that can help improve our understanding of the effect of different statistical distributions on the randomness of the noise vectors for enhancing the diversity and quality of Generative AI models. The proposed modeling and simulation framework currently adapts eight statistical distributions, including Gaussian, Cauchy, Beta, Laplace, and Gamma distributions to draw different types of noise vectors and utilize them to generate bird images for studying their contributions to diversity and quality. A pretrained BigGAN is also used as Generative AI model. A diversity-quality-index (DQI) that utilizes k-means clustering and mean squared error (MSE) is used to measure the diversity and quality of 200 BigGAN-generated images. Simulations show that BigGAN can achieve significant diversity with the help of multiple statistical distributions. Simulations also show that the shape of a distribution, considering Gaussian as a reference distribution, influences randomness of the noise vectors associated with the latent space of images.