Composite Active Learning

Composite learning encompasses a range of techniques that improve machine learning model performance by strategically combining multiple learning tasks or data sources. Current research focuses on enhancing active learning in multi-domain settings, improving federated learning with heterogeneous data, and leveraging self-supervised auxiliary tasks to boost target task accuracy and robustness. These advancements aim to address challenges in data efficiency, model generalization, and handling diverse data distributions, ultimately leading to more effective and robust machine learning systems across various applications.