Cross Supervision

Cross-supervision is a machine learning technique that leverages the information from multiple, often independently trained, models or data sources to improve model performance, particularly in scenarios with limited labeled data. Current research focuses on applying cross-supervision in various contexts, including semi-supervised learning, few-shot learning, and handling noisy data, often employing techniques like knowledge distillation, dual classifiers, and cross-risk minimization. This approach shows promise in improving model robustness and generalization, particularly in resource-constrained settings such as medical image analysis and remote sensing, by effectively utilizing unlabeled data and mitigating the impact of annotation errors.