Domain Diversity

Domain diversity, encompassing the variability within and between datasets used to train machine learning models, is a crucial challenge across various fields. Current research focuses on enhancing model robustness and fairness by addressing issues like data imbalance and inconsistent update directions in federated learning, and by developing methods to synthesize diverse training data using techniques such as diffusion models and data augmentation. This work aims to improve the generalization capabilities of models, leading to more reliable and equitable performance across different applications, such as activity recognition, person re-identification, and sentiment analysis. Addressing domain diversity is vital for building more robust and generalizable AI systems.