  Deep learning faces a formidable challenge when handling noisy labels, as
models tend to overfit samples affected by label noise. This challenge is
further compounded by the presence of instance-dependent noise (IDN), a
realistic form of label noise arising from ambiguous sample information. To
address IDN, Label Noise Learning (LNL) incorporates a sample selection stage
to differentiate clean and noisy-label samples. This stage uses an arbitrary
criterion and a pre-defined curriculum that initially selects most samples as
noisy and gradually decreases this selection rate during training. Such
curriculum is sub-optimal since it does not consider the actual label noise
rate in the training set. This paper addresses this issue with a new noise-rate
estimation method that is easily integrated with most state-of-the-art (SOTA)
LNL methods to produce a more effective curriculum. Synthetic and real-world
benchmark results demonstrate that integrating our approach with SOTA LNL
methods improves accuracy in most cases.
