Learning With Single-Teacher Multi-Student


  • Shan You Peking University
  • Chang Xu University of Sydney
  • Chao Xu Peking University
  • Dacheng Tao University of Sydney


multiclass classification, binary classification, teacher student


In this paper we study a new learning problem defined as "Single-Teacher Multi-Student" (STMS) problem, which investigates how to learn a series of student (simple and specific) models from a single teacher (complex and universal) model. Taking the multiclass and binary classification for example, we focus on learning multiple binary classifiers from a single multiclass classifier, where each of binary classifier is responsible for a certain class. This actually derives from some realistic problems, such as identifying the suspect based on a comprehensive face recognition system. By treating the already-trained multiclass classifier as the teacher, and multiple binary classifiers as the students, we propose a gated support vector machine (gSVM) as a solution. A series of gSVMs are learned with the help of single teacher multiclass classifier. The teacher's help is two-fold; first, the teacher's score provides the gated values for students' decision; second, the teacher can guide the students to accommodate training examples with different difficulty degrees. Extensive experiments on real datasets validate its effectiveness.




How to Cite

You, S., Xu, C., Xu, C., & Tao, D. (2018). Learning With Single-Teacher Multi-Student. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11636