Estimating Noise Rates to Improve Noisy-Label Learning with Instance-Dependent Noise

The core message of this paper is to propose a novel graphical model that estimates the label noise rate from the training data and leverages this estimate to refine the sample selection curriculum, thereby improving the performance of state-of-the-art noisy-label learning methods on both synthetic and real-world benchmarks with instance-dependent noise.