Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a
lightweight student model, has been investigated to design efficient neural architectures …

Abstract Knowledge distillation (KD) is a general neural network training approach that uses
a teacher model to guide the student model. Existing works mainly study KD from the …

Knowledge distillation is an effective approach to leverage a well-trained network or an
ensemble of them, named as the teacher, to guide the training of a student network. The …

Abstract Knowledge distillation (KD) is widely applied in the training of efficient neural
network. A compact model, which is trained to mimic the representation of a cumbersome …

Abstract Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed
neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon …

Abstract Knowledge distillation (KD) can effectively compress neural networks by training a
smaller network (student) to simulate the behavior of a larger one (teacher). A counter …

Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while
having a fixed capacity budget. It is a commonly used technique for model compression …

Abstract Knowledge distillation (KD) has proven to be a highly effective approach for
enhancing model performance through a teacher-student training scheme. However, most …

Knowledge distillation (KD) is commonly deemed as an effective model compression
technique in which a compact model (student) is trained under the supervision of a larger …

Abstract Knowledge distillation involves transferring soft labels from a teacher to a student
using a shared temperature-based softmax function. However the assumption of a shared …