Instance Dependent Label Noise

Instance-dependent label noise (IDN), where the probability of a mislabeled data point depends on its features, poses a significant challenge to machine learning model training. Current research focuses on developing robust algorithms that can effectively learn from such noisy data, often employing techniques like Bayesian ensembles, graphical models, and manifold regularization to estimate the complex relationships between features and label errors, or by leveraging small sets of reliably labeled data. Addressing IDN is crucial for improving the reliability and generalizability of models trained on real-world datasets, which frequently contain label inaccuracies that are not randomly distributed. This research directly impacts the development of more robust and trustworthy AI systems across various applications.