Hard to Classify Instance

Hard-to-classify instances, or outliers, pose a significant challenge across various machine learning domains, hindering model accuracy and generalization. Current research focuses on identifying and effectively utilizing these instances during training, employing techniques like masked hard instance mining within multiple instance learning (MIL) frameworks and specialized batch sampling strategies for contrastive learning. These approaches aim to improve model robustness and discriminative power by explicitly addressing the limitations of focusing solely on easily classified examples. The improved understanding and handling of hard-to-classify instances is crucial for advancing the reliability and performance of machine learning models in diverse applications, from medical image analysis to natural language processing.