Generalization Capacity

Generalization capacity in machine learning refers to a model's ability to accurately predict outcomes on unseen data, a crucial aspect for reliable real-world applications. Current research focuses on understanding and improving generalization across diverse model architectures, including deep neural networks (DNNs), transformers, and graph neural networks, often employing techniques like regularization, multi-task learning, and data-centric approaches to enhance performance. This research is vital for improving the robustness and reliability of AI systems across various domains, from medical image analysis and earth observation to natural language processing and federated learning, where models must handle data heterogeneity and potential adversarial attacks. A key challenge remains developing accurate and practical metrics for evaluating generalization, particularly in out-of-distribution scenarios.