Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation

Understand

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.