Noisy Annotation

Noisy annotation, the presence of errors or inconsistencies in training data labels, is a significant challenge across various machine learning domains, hindering model accuracy and generalization. Current research focuses on developing robust algorithms and model architectures that can effectively learn from noisy data, including methods leveraging multiple annotators, self-correction techniques, and active learning strategies to improve label quality. Addressing noisy annotation is crucial for improving the reliability and performance of machine learning models in diverse applications, from medical image analysis and remote sensing to natural language processing and object detection, where obtaining perfectly clean labels is often impractical or prohibitively expensive.