Worst Class

Research on "worst-class" performance focuses on improving the accuracy of machine learning models on the classes they struggle most with, addressing a critical limitation in real-world applications where all classes must be treated equally. Current efforts involve developing new training algorithms, such as boosting and class-focused online learning, and data sampling techniques like class priority reweighting, to enhance robustness and reduce the performance gap between best and worst-performing classes in various models, including large language models and visual classifiers. This research is crucial for building reliable and trustworthy AI systems, particularly in safety-critical domains where failures in even one class can have severe consequences.