Noisy Example

Noisy examples, or incorrectly labeled data points, significantly hinder the performance and generalization of machine learning models, particularly in large-scale datasets. Current research focuses on developing methods to detect and mitigate the impact of noisy labels, employing techniques like analyzing training dynamics, using active learning strategies for label correction, and leveraging ensemble methods to identify inconsistencies in model predictions across different networks. These advancements are crucial for improving the reliability and robustness of machine learning systems across various applications, from natural language processing to speech recognition, where noisy data is prevalent.