LAUSR

Knowledge distillation aims at transferring the knowledge from a pre-trained complex model, called teacher, to a relatively smaller and faster one, called student. Unlike previous works that transfer the teacher’s softened distributions or feature spaces, in this paper, we propose a novel approach, called Virtual Knowledge Distillation (VKD), that transfers a softened distribution generated by a virtual knowledge generator conditioned on class label. A virtual knowledge generator is trained independently, but concurrently with a teacher, to mimic the teacher’s softened distributions. Afterwards, when training a student, virtual knowledge generator can be exploited instead of the teacher’s softened distributions or combined with the existing distillation methods in a straightforward manner. Moreover, with slight modifications, VKD can be utilized not only for the self-knowledge distillation method but also for the collaborative learning method. We compare our method with several representative distillation methods in various combinations of teacher and student architectures on the image classification tasks. Experimental results on various image classification tasks demonstrate that VKD show a competitive performance compared to the conventional distillation methods, and when combined with them, the performance is improved with a substantial margin.