Worst Group Generalization

Worst-group generalization focuses on improving the performance of machine learning models on underrepresented subgroups within a dataset, addressing the issue of models achieving high overall accuracy while failing on specific groups. Current research investigates techniques like data downsampling with class reweighting, data augmentation methods such as mixup, and the impact of model size and architecture (including neural networks and deep metric learning models) on mitigating this problem. Understanding and addressing worst-group generalization is crucial for building robust and fair machine learning systems, particularly in applications with sensitive or ethically significant implications.