In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Knowledge distillation (KD) is a powerful and widely applicable technique for the
compression of deep learning models. The main idea of knowledge distillation is to transfer …

Deep learning based models are relatively large, and it is hard to deploy such models on
resource-limited devices such as mobile phones and embedded devices. One possible …

This paper addresses the problem of model compression via knowledge distillation. We
advocate for a method that optimizes the output feature of the penultimate layer of the …

Abstract Knowledge distillation aims to compress a powerful yet cumbersome teacher model
into a lightweight student model without much sacrifice of performance. For this purpose …

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 …

Significant memory and computational requirements of large deep neural networks restrict
their application on edge devices. Knowledge distillation (KD) is a prominent model …

Abstract Knowledge distillation is considered as a training and compression strategy in
which two neural networks, namely a teacher and a student, are coupled together during …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Abstract Knowledge distillation transfers the knowledge from a cumbersome teacher to a
small student. Recent results suggest that the student-friendly teacher is more appropriate to …