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With the concept of teaching being introduced to the machine learningcommunity, a teacher model start using dynamic loss functions to teach thetraining of a student model. The dynamic intends to set adaptive loss functionsto different phases of student model learning. In existing works, the teachermodel 1) merely determines the loss function based on the present states of thestudent model, i.e., disregards the experience of the teacher; 2) only utilizesthe states of the student model, e.g., training iteration number andloss/accuracy from training/validation sets, while ignoring the states of theloss function. In this paper, we first formulate the loss adjustment as atemporal task by designing a teacher model with memory units, and, therefore,enables the student learning to be guided by the experience of the teachermodel. Then, with a dynamic loss network, we can additionally use the states ofthe loss to assist the teacher learning in enhancing the interactions betweenthe teacher and the student model. Extensive experiments demonstrate ourapproach can enhance student learning and improve the performance of variousdeep models on real-world tasks, including classification, objective detection,and semantic segmentation scenarios.

L2T-DLN: Learning to Teach with Dynamic Loss Network