Style Based Generative Adversarial Network

Style-based Generative Adversarial Networks (StyleGANs) are a class of deep learning models designed to generate high-quality, diverse images and other data by leveraging disentangled latent spaces. Current research focuses on applying StyleGANs to diverse applications, including image completion, data augmentation for medical imaging (e.g., PET scans and facial cleft representations), and novel view synthesis for 3D modeling, often incorporating techniques like style mixing and modulation to enhance control and realism. This approach offers significant advantages in areas where data is scarce or high-quality data generation is crucial, impacting fields ranging from medical imaging and computer vision to text-to-speech synthesis.