  Learning with noisy labels (LNL) aims to train a high-performing model using
a noisy dataset. We observe that noise for a given class often comes from a
limited set of categories, yet many LNL methods overlook this. For example, an
image mislabeled as a cheetah is more likely a leopard than a hippopotamus due
to its visual similarity. Thus, we explore Learning with Noisy Labels with
noise source Knowledge integration (LNL+K), which leverages knowledge about
likely source(s) of label noise that is often provided in a dataset's
meta-data. Integrating noise source knowledge boosts performance even in
settings where LNL methods typically fail. For example, LNL+K methods are
effective on datasets where noise represents the majority of samples, which
breaks a critical premise of most methods developed for LNL. Our LNL+K methods
can boost performance even when noise sources are estimated rather than
extracted from meta-data. We provide several baseline LNL+K methods that
integrate noise source knowledge into state-of-the-art LNL models that are
evaluated across six diverse datasets and two types of noise, where we report
gains of up to 23% compared to the unadapted methods. Critically, we show that
LNL methods fail to generalize on some real-world datasets, even when adapted
to integrate noise source knowledge, highlighting the importance of directly
exploring LNL+K.
