Neural Network Generalization

Neural network generalization focuses on understanding why and how deep learning models trained on a limited dataset can accurately predict outcomes on unseen data. Current research investigates factors influencing generalization, such as model architecture (including modular networks and transformers), training optimization techniques (like sharpness-aware minimization and data augmentation strategies), and the role of biases and noise in both data and model parameters. These investigations are crucial for improving the reliability and robustness of AI systems across diverse applications, ranging from medical image analysis to robotics and cybersecurity, where generalization to real-world scenarios is paramount.