  Label noise, commonly found in real-world datasets, has a detrimental impact
on a model's generalization. To effectively detect incorrectly labeled
instances, previous works have mostly relied on distinguishable training
signals, such as training loss, as indicators to differentiate between clean
and noisy labels. However, they have limitations in that the training signals
incompletely reveal the model's behavior and are not effectively generalized to
various noise types, resulting in limited detection accuracy. In this paper, we
propose DynaCor framework that distinguishes incorrectly labeled instances from
correctly labeled ones based on the dynamics of the training signals. To cope
with the absence of supervision for clean and noisy labels, DynaCor first
introduces a label corruption strategy that augments the original dataset with
intentionally corrupted labels, enabling indirect simulation of the model's
behavior on noisy labels. Then, DynaCor learns to identify clean and noisy
instances by inducing two clearly distinguishable clusters from the latent
representations of training dynamics. Our comprehensive experiments show that
DynaCor outperforms the state-of-the-art competitors and shows strong
robustness to various noise types and noise rates.
