Establishment of Neural Networks Robust to Label Noise
This work addresses label noise robustness in deep learning for image classification, but it is incremental as it builds on existing methods without achieving significant new gains.
The paper tackled label noise in deep neural networks for image classification by developing a transition matrix estimator and testing it on FashionMNIST datasets, showing robustness in LeNet and AlexNet models but failing to demonstrate clear improvements from noise correction due to tuning limitations.
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.