Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
This work addresses the challenge of realistic label noise for machine learning practitioners, but it is incremental as it builds upon existing state-of-the-art methods.
The paper tackles the problem of instance-dependent noisy labels in deep learning by proposing a new noise-rate estimation method that improves the curriculum for sample selection in label noise learning methods, leading to accuracy improvements in synthetic and real-world benchmarks.
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.