  We introduce a novel method for training machine learning models in the
presence of noisy labels, which are prevalent in domains such as medical
diagnosis and autonomous driving and have the potential to degrade a model's
generalization performance. Inspired by established literature that highlights
how deep learning models are prone to overfitting to noisy samples in the later
epochs of training, we propose a strategic approach. This strategy leverages
the distance to class centroids in the latent space and incorporates a
discounting mechanism, aiming to diminish the influence of samples that lie
distant from all class centroids. By doing so, we effectively counteract the
adverse effects of noisy labels. The foundational premise of our approach is
the assumption that samples situated further from their respective class
centroid in the initial stages of training are more likely to be associated
with noise. Our methodology is grounded in robust theoretical principles and
has been validated empirically through extensive experiments on several
benchmark datasets. Our results show that our method consistently outperforms
the existing state-of-the-art techniques, achieving significant improvements in
classification accuracy in the presence of noisy labels. The code for our
proposed loss function and supplementary materials is available at
https://github.com/wanifarooq/NCOD
