  This study explores the robustness of label noise classifiers, aiming to
enhance model resilience against noisy data in complex real-world scenarios.
Label noise in supervised learning, characterized by erroneous or imprecise
labels, significantly impairs model performance. This research focuses on the
increasingly pertinent issue of label noise's impact on practical applications.
Addressing the prevalent challenge of inaccurate training data labels, we
integrate adversarial machine learning (AML) and importance reweighting
techniques. Our approach involves employing convolutional neural networks (CNN)
as the foundational model, with an emphasis on parameter adjustment for
individual training samples. This strategy is designed to heighten the model's
focus on samples critically influencing performance.
