Noisy Label Detection

Noisy label detection aims to identify and mitigate the impact of inaccurate labels in training datasets, a pervasive problem hindering the performance and robustness of machine learning models. Current research focuses on developing methods that leverage model inconsistencies during training (e.g., comparing predictions across different training epochs or model ensembles), statistical metrics to identify outliers, and the integration of vision-language models or generative adversarial networks for improved detection accuracy. Successfully addressing noisy labels is crucial for enhancing the reliability and generalizability of machine learning models across diverse applications, from image classification and speaker recognition to more sensitive domains like medical diagnosis.