Label Ambiguity

Label ambiguity, the uncertainty in assigning correct labels to data points, poses a significant challenge across various machine learning tasks, particularly in image classification, object detection, and speech recognition. Current research focuses on developing robust models and algorithms, such as distributionally robust SVMs and uncertainty-aware sampling methods, to mitigate the negative impact of ambiguous labels, often employing techniques like self-training, pseudo-labeling, and graph convolutional networks. Addressing label ambiguity is crucial for improving the reliability and generalizability of machine learning models, leading to more accurate and trustworthy predictions in real-world applications. This is particularly important in high-stakes domains like medical image analysis where misclassifications can have serious consequences.