Noisy Class

Noisy class problems arise when training data contains inaccurate or unreliable class labels, hindering the performance of machine learning models. Current research focuses on developing robust methods to handle these noisy labels, including techniques that leverage multiple annotators, incorporate additional information like part-level labels, or utilize cross-modal data to improve label confidence. These advancements aim to improve the accuracy and reliability of models trained on real-world data, where imperfect labeling is common, impacting diverse fields such as image classification, video analysis, and remote sensing. The ultimate goal is to build more resilient and generalizable machine learning systems.