In the development of machine learning models, we often presume that our training data represents ground truth—a flawless reflection of reality against which our models learn to make predictions. However, this assumption rarely holds in real-world applications. Label noise is virtually unavoidable in large-scale datasets due to various factors, such as the lack of expertise from annotators, ambiguous labeling guidelines, inherent uncertainty in the labeling process itself, etc.