Hard Sample

"Hard samples," in machine learning, refer to data points that are difficult for models to classify correctly, often due to inherent ambiguity or noise. Current research focuses on identifying and utilizing these samples to improve model robustness and generalization, employing techniques like learned reweighting, meta-learning, and adversarial training across various model architectures (e.g., deep neural networks, graph neural networks). Understanding and addressing the challenges posed by hard samples is crucial for enhancing the reliability and performance of machine learning models across diverse applications, from medical image analysis to natural language processing.