Co Teaching

Co-teaching is a machine learning technique employing two or more models that collaboratively learn from each other, often using pseudo-labels generated by the models themselves. Current research focuses on refining co-teaching strategies for various tasks, including semi-supervised and unsupervised learning, noisy label handling, and domain adaptation, often incorporating novel architectures like transformer networks and employing techniques such as adaptive pseudo-labeling and consistent sample mining. These advancements aim to improve model generalization, reduce reliance on large labeled datasets, and enhance robustness to noisy data, impacting fields ranging from image recognition and video anomaly detection to medical image segmentation and space target detection.