Label Denoising

Label denoising focuses on improving the accuracy of machine learning models by mitigating the negative effects of noisy or erroneous labels in training data. Current research emphasizes developing novel algorithms and model architectures, such as contrastive learning, teacher-student networks, and self-supervised learning approaches, to identify and correct noisy labels across diverse applications. This work is crucial for advancing the reliability and performance of machine learning models in various fields, including medical image analysis, human action recognition, and financial time series prediction, where obtaining perfectly labeled data is often impractical or impossible. The resulting improvements in model accuracy and robustness have significant implications for the reliability of AI-driven decision-making in these and other domains.