Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the
dependence of original training data, and has recently achieved impressive results in …

Data-free knowledge distillation (DFKD) has recently been attracting increasing attention
from research communities, attributed to its capability to compress a model only using …

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 (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 …

Data-free knowledge distillation is a challenging model lightweight task for scenarios in
which the original dataset is not available. Previous methods require a lot of extra …

To put a state-of-the-art neural network to practical use, it is necessary to design a model
that has a good trade-off between the resource consumption and performance on the test …

Distillation in neural networks using only the samples randomly drawn from a Gaussian
distribution is possibly the most straightforward solution one can think of for the complex …

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 …

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 (KD) is an efficient framework for compressing large-scale pre-trained
language models. Recent years have seen a surge of research aiming to improve KD by …