Mutual Learning

Mutual learning, a machine learning paradigm, focuses on improving multiple models by enabling them to learn from each other's strengths, thereby surpassing the performance of individually trained models. Current research emphasizes applications across diverse fields, employing various architectures including Bayesian neural networks, transformer models, and deep learning frameworks tailored for specific tasks like image segmentation, natural language processing, and robotic control. This approach holds significant promise for enhancing model accuracy and robustness in various applications, particularly where data is scarce or heterogeneous, and for fostering more effective human-AI collaboration.