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Knowledge distillation (KD) is a new method for transferring knowledge of astructure under training to another one. The typical application of KD is inthe form of learning a small model (named as a student) by soft labels producedby a complex model (named as a teacher). Due to the novel idea introduced inKD, recently, its notion is used in different methods such as compression andprocesses that are going to enhance the model accuracy. Although differenttechniques are proposed in the area of KD, there is a lack of a model togeneralize KD techniques. In this paper, various studies in the scope of KD areinvestigated and analyzed to build a general model for KD. All the methods andtechniques in KD can be summarized through the proposed model. By utilizing theproposed model, different methods in KD are better investigated and explored.The advantages and disadvantages of different approaches in KD can be betterunderstood and develop a new strategy for KD can be possible. Using theproposed model, different KD methods are represented in an abstract view.

Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation