Exemplar Selection

Exemplar selection focuses on choosing the most effective subset of training data to improve the performance of machine learning models, particularly in few-shot learning and continual learning scenarios. Current research emphasizes developing algorithms that optimize exemplar choice based on semantic similarity, structural relationships (e.g., using graph representations), and the impact of exemplar ordering on model performance, often employing techniques like spectral clustering, neural bandits, and linear programming. These advancements are significant because effective exemplar selection can enhance model efficiency, reduce catastrophic forgetting in continual learning, and improve fairness and robustness in applications ranging from natural language processing to 3D object recognition and image generation.