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.

翻译：本研究探讨了标签噪声分类器的鲁棒性，旨在增强模型在复杂现实场景中对噪声数据的抗干扰能力。在监督学习中，标签噪声表现为错误或不精确的标签，会显著损害模型性能。本研究聚焦于标签噪声对实际应用日益突出的影响问题。针对训练数据标签不准确这一普遍挑战，我们整合了对抗性机器学习（AML）和重要性重加权技术。该方法以卷积神经网络（CNN）为基础模型，重点强调对单个训练样本进行参数调整。这一策略旨在提升模型对影响性能的关键样本的关注程度。