Robust Dataset

Robust dataset research focuses on creating training data that leads to machine learning models less susceptible to errors and more reliable in diverse conditions. Current efforts concentrate on developing methods to identify and mitigate vulnerabilities in datasets, including addressing issues like data contamination, feature skew from federated learning, and the impact of data heterogeneity on model robustness. This work is crucial for improving the trustworthiness and reliability of AI systems across various applications, from autonomous driving to medical diagnosis, where model robustness is paramount for safety and efficacy. Ongoing research explores both data augmentation techniques and novel training algorithms to achieve this robustness.