False Negative

False negatives, the failure to correctly identify a positive instance, represent a significant challenge across diverse machine learning applications. Current research focuses on mitigating this issue through improved data sampling techniques (e.g., eliminating "false negatives" from training sets), refined model architectures (like contrastive learning and boosting algorithms), and the development of methods to identify and correct for false negatives during inference. Addressing false negatives is crucial for improving the reliability and trustworthiness of machine learning systems, particularly in high-stakes domains such as medical diagnosis and autonomous driving, where missed detections can have severe consequences.