High Quality Synthesis

High-quality synthesis aims to generate realistic and diverse data, spanning images, audio, and tabular data, efficiently and with high fidelity. Current research focuses on improving the speed and quality of diffusion models through distillation techniques, such as single-fold distillation, which reduces the number of sampling steps needed while maintaining output quality. Other approaches leverage generative adversarial networks (GANs) and language modeling techniques for audio generation, addressing challenges like privacy in distributed data settings and the need for data augmentation in specific applications (e.g., dysarthric speech synthesis). These advancements have significant implications for various fields, including data augmentation for machine learning, medical imaging, and the creation of realistic synthetic data for diverse applications.