Domain Information

Domain information, encompassing the characteristics and biases inherent in datasets, is a crucial consideration in machine learning, particularly when models are trained on one dataset and applied to another (domain generalization). Current research focuses on developing methods to control or remove domain-specific information during inference, using techniques like controllable gate adapters and hypernetwork-based mixtures of experts, to improve model generalization and robustness. This work is significant because it addresses the limitations of models trained on non-IID data, leading to more reliable and adaptable machine learning systems across diverse applications, including acoustic scene classification and federated learning on graphs. The ability to identify and manage domain information also enhances the interpretability and efficiency of machine learning models.