Mentored Learning: Improving Generalization and Convergence of Student Learner via Teaching Feedback

Student learners typically engage in an iterative process of actively updating its hypotheses, like active learning. While this behavior can be advantageous, there is an inherent risk of introducing mistakes through incremental updates including weak initialization, inaccurate or insignificant history states, resulting in expensive convergence cost. In this work, rather than solely monitoring the update of the learner's status, we propose monitoring the disagreement w.r.t. F T (<middle dot>) between the learner and teacher, and call this new paradigm "Mentored Learning{''