Cross Pseudo Supervision

Cross pseudo supervision (CPS) is a semi-supervised learning technique aiming to improve model performance by leveraging unlabeled data alongside limited labeled data. Current research focuses on enhancing CPS's robustness and accuracy, particularly for challenging tasks like medical image segmentation and geospatial image analysis, often employing variations of teacher-student frameworks, consistency regularization, and active learning strategies within models such as nnU-Net and transformer-based architectures. These advancements are significant because they enable the training of high-performing models in scenarios with scarce labeled data, reducing annotation costs and expanding the applicability of machine learning to diverse domains.