Generative Sampling

Generative sampling focuses on creating new data instances that resemble a given dataset, aiming to improve data efficiency and address challenges like class imbalance and data scarcity. Current research emphasizes the use of diffusion models, variational autoencoders (VAEs), and normalizing flows, often incorporating techniques like knowledge distillation and minority guidance to enhance sample quality and diversity. These advancements are impacting various fields, including image classification, federated learning, and medical imaging, by enabling data augmentation, improving model robustness, and facilitating the generation of synthetic data for under-represented classes. The ultimate goal is to generate high-fidelity, diverse samples that are useful for downstream tasks and overcome limitations of existing data.