Co Training

Co-training is a semi-supervised machine learning technique that leverages the complementary strengths of multiple models trained on different views or subsets of data to improve learning from limited labeled examples. Current research focuses on enhancing co-training's effectiveness through diverse strategies, including incorporating uncertainty estimations, employing hierarchical expert models, and designing novel architectures like dual-space or single-branch networks for specific tasks such as image segmentation and natural language processing. This approach is particularly valuable in domains with scarce labeled data, such as medical image analysis and open-domain natural language understanding, offering significant improvements in model performance and efficiency compared to solely supervised methods.