Label Confidence

Label confidence, the degree of certainty associated with a model's prediction, is a crucial aspect of machine learning, particularly in scenarios with noisy or incomplete labels. Current research focuses on improving label confidence estimation through various techniques, including novel co-training architectures, data augmentation coupled with label smoothing, and graph-based methods leveraging data topology. These advancements aim to enhance model robustness, uncertainty quantification, and ultimately, the reliability of predictions across diverse applications, from image classification to reinforcement learning.