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In human learning, an effective skill in improving learning outcomes islearning by teaching: a learner deepens his/her understanding of a topic byteaching this topic to others. In this paper, we aim to borrow thisteaching-driven learning methodology from humans and leverage it to train moreperformant machine learning models, by proposing a novel ML framework referredto as learning by teaching (LBT). In the LBT framework, a teacher modelimproves itself by teaching a student model to learn well. Specifically, theteacher creates a pseudo-labeled dataset and uses it to train a student model.Based on how the student performs on a validation dataset, the teacherre-learns its model and re-teaches the student until the student achieves greatvalidation performance. Our framework is based on three-level optimizationwhich contains three stages: teacher learns; teacher teaches student; teacherre-learns based on how well the student performs. A simple but efficientalgorithm is developed to solve the three-level optimization problem. We applyLBT to search neural architectures on CIFAR-10, CIFAR-100, and ImageNet. Theefficacy of our method is demonstrated in various experiments.

Learning by Teaching, with Application to Neural Architecture Search