Supervised Correction

Supervised correction focuses on improving the accuracy of machine learning models by addressing errors in training data or model predictions. Current research emphasizes developing algorithms that iteratively refine labels or predictions, often employing techniques like mean teacher models, self-supervised learning, and attention mechanisms within deep learning architectures to identify and correct inconsistencies. These methods are proving valuable across diverse applications, including medical image segmentation, remote sensing, and image super-resolution, by enhancing model performance and reducing reliance on perfectly annotated datasets. The resulting improvements in accuracy and efficiency have significant implications for various scientific fields and practical applications.