  Learning with noisy labels (LNL) is challenging as the model tends to
memorize noisy labels, which can lead to overfitting. Many LNL methods detect
clean samples by maximizing the similarity between samples in each category,
which does not make any assumptions about likely noise sources. However, we
often have some knowledge about the potential source(s) of noisy labels. For
example, an image mislabeled as a cheetah is more likely a leopard than a
hippopotamus due to their visual similarity. Thus, we introduce a new task
called Learning with Noisy Labels and noise source distribution Knowledge
(LNL+K), which assumes we have some knowledge about likely source(s) of label
noise that we can take advantage of. By making this presumption, methods are
better equipped to distinguish hard negatives between categories from label
noise. In addition, this enables us to explore datasets where the noise may
represent the majority of samples, a setting that breaks a critical premise of
most methods developed for the LNL task. We explore several baseline LNL+K
approaches that integrate noise source knowledge into state-of-the-art LNL
methods across three diverse datasets and three types of noise, where we report
a 5-15% boost in performance compared with the unadapted methods. Critically,
we find that LNL methods do not generalize well in every setting, highlighting
the importance of directly exploring our LNL+K task.

Computer Vision and Pattern Recognition