  Class labels used for machine learning are relatable to each other, with
certain class labels being more similar to each other than others (e.g. images
of cats and dogs are more similar to each other than those of cats and cars).
Such similarity among classes is often the cause of poor model performance due
to the models confusing between them. Current labeling techniques fail to
explicitly capture such similarity information. In this paper, we instead
exploit the similarity between classes by capturing the similarity information
with our novel confidence labels. Confidence labels are probabilistic labels
denoting the likelihood of similarity, or confusability, between the classes.
Often even after models are trained to differentiate between classes in the
feature space, the similar classes' latent space still remains clustered. We
view this type of clustering as valuable information and exploit it with our
novel projective loss functions. Our projective loss functions are designed to
work with confidence labels with an ability to relax the loss penalty for
errors that confuse similar classes. We use our approach to train neural
networks with noisy labels, as we believe noisy labels are partly a result of
confusability arising from class similarity. We show improved performance
compared to the use of standard loss functions. We conduct a detailed analysis
using the CIFAR-10 dataset and show our proposed methods' applicability to
larger datasets, such as ImageNet and Food-101N.
