Label noise is a significant obstacle in deep learning model training. It can have a considerable impact on the performance of image classification models, particularly deep neural networks, which are especially susceptible because they have a strong propensity to memorise noisy labels. In this paper, we have examined the fundamental concept underlying related label noise approaches. A transition matrix estimator has been created, and its effectiveness against the actual transition matrix has been demonstrated. In addition, we examined the label noise robustness of two convolutional neural network classifiers with LeNet and AlexNet designs. The two FashionMINIST datasets have revealed the robustness of both models. We are not efficiently able to demonstrate the influence of the transition matrix noise correction on robustness enhancements due to our inability to correctly tune the complex convolutional neural network model due to time and computing resource constraints. There is a need for additional effort to fine-tune the neural network model and explore the precision of the estimated transition model in future research.

翻译：标签噪声是深度学习模型训练中的一个重要障碍。它可能对图像分类模型的性能产生显著影响，尤其是深度神经网络，因为它们极易受此影响，具有强烈的记忆噪声标签的倾向。在本文中，我们研究了相关标签噪声方法的基本概念。我们构建了一个转移矩阵估计器，并证明其相对于真实转移矩阵的有效性。此外，我们考察了两种采用LeNet和AlexNet设计的卷积神经网络分类器对标签噪声的鲁棒性。两个FashionMNIST数据集揭示了两类模型的鲁棒性。由于时间和计算资源限制，我们未能正确调整复杂的卷积神经网络模型，因此无法有效展示转移矩阵噪声校正对鲁棒性提升的影响。未来的研究需要进一步努力来微调神经网络模型，并探索所估计转移模型的精度。