1 research outputs found
Establishment of Neural Networks Robust to Label Noise
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.Comment: 11 pages, 7 figure