Model Generalization

Model generalization, the ability of a machine learning model to perform well on unseen data, is a central challenge in the field. Current research focuses on improving generalization through techniques like sharpness-aware minimization (finding flatter minima in the loss landscape), data augmentation (especially learnable augmentation to address bias), and coreset selection (using influence functions to identify the most informative training data). These efforts, often applied to various architectures including large language models and convolutional neural networks, aim to enhance model robustness and reliability across diverse datasets and real-world applications, ultimately leading to more trustworthy and effective AI systems.