Improved Knowledge Distillation via Teacher Assistant


Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller ( student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10, CIFAR-100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

Iman Mirzadeh
Research Assistant

I am a PhD student and Graduate Research Assistant at the Washington State University Embedded and Pervasive Systems Laboratory (EPSL) under supervision of Dr. Hassan Ghasemzadeh. I am interested in the real-world challenges of working with machine learning models such as energy constraints and human-in-the-loop interactions with these models. Specifically, I am focusing on Model Optimization (such as model compression), where my goal is to build more efficient models or use the existing models more efficiently. Before joining EPSL, I was an ML Engineer at Sokhan AI, where we provided accurate and scalable Natural Language Processing (NLP) and Computer Vision (CV) services to businesses.