Clean Distribution

Clean distribution research focuses on improving the robustness and reliability of machine learning models by addressing issues arising from data corruption, heterogeneity, and out-of-distribution (OOD) samples. Current research emphasizes developing methods to learn clean data distributions from corrupted sources, often leveraging diffusion models and normalizing flows, and employing techniques like inpainting and ensemble distribution distillation to enhance OOD detection. These advancements are crucial for improving the generalization and safety of AI systems across diverse applications, particularly in domains with limited clean data or high variability in data distributions, such as medical imaging and federated learning.