Recent successes suggest that knowledge distillation techniques can usefully transfer
knowledge between deep neural networks as compression and acceleration techniques, eg …

In this research, we propose an innovative method to boost Knowledge Distillation efficiency
without the need for resource-heavy teacher models. Knowledge Distillation trains a …

Knowledge distillation field delicately designs various types of knowledge to shrink the
performance gap between compact student and large-scale teacher. These existing …

As deep neural networks are widely used with computer vision, Model Compression
Methods for knowledge distillation are being actively investigated in order to deploy them …

Knowledge Distillation is a technique which aims to utilize dark knowledge to compress and
transfer information from a vast, well-trained neural network (teacher model) to a smaller …

Recent advances have indicated the strengths of self-supervised pre-training for improving
representation learning on downstream tasks. Existing works often utilize self-supervised …

Knowledge distillation is a common method of model compression, which uses large models
(teacher networks) to guide the training of small models (student networks). However, the …

Abstract Knowledge distillation is a widely applicable technique for supervising the training
of a light-weight student neural network by capturing and transferring the knowledge of a …

Knowledge distillation aims at transferring sufficient knowledge from one cumbersome
teacher network to another compressed student network. Most previous knowledge …

Knowledge Distillation (KD) aims at improving the performance of a low-capacity student
model by inheriting knowledge from a high-capacity teacher model. Previous KD methods …