Densely Guided Knowledge Distillation using Multiple Teacher Assistants

With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for model compression and transfer learning. However, few studies have been performed to resolve the poor learning issue of the student network when the student and teacher model sizes significantly differ. In this paper, we propose a densely guided knowledge distillation using multiple teacher assistants that gradually decrease the model size to efficiently bridge the gap between teacher and student networks. To stimulate more efficient learning of the student network, we guide each teacher assistant to every other smaller teacher assistant step by step. Specifically, when teaching a smaller teacher assistant at the next step, the existing larger teacher assistants from the previous step are used as well as the teacher network to increase the learning efficiency. Moreover, we design stochastic teaching where, for each mini-batch during training, a teacher or a teacher assistant is randomly dropped. This acts as a regularizer like dropout to improve the accuracy of the student network. Thus, the student can always learn rich distilled knowledge from multiple sources ranging from the teacher to multiple teacher assistants. We verified the effectiveness of the proposed method for a classification task using Cifar-10, Cifar-100, and Tiny ImageNet. We also achieved significant performance improvements with various backbone architectures such as a simple stacked convolutional neural network, ResNet, and WideResNet.