Intermediate layer matching is shown as an effective approach for improving knowledge
distillation (KD). However, this technique applies matching in the hidden spaces of two …

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

Pre-trained language models (PLMs), such as BERT, have achieved outstanding
performance on multiple natural language processing (NLP) tasks. However, such pre …

Traditional knowledge distillation approaches are typically designed for specific tasks, as
they primarily distilling deep features from intermediate layers of a neural network, generally …

Existing knowledge distillation methods directly transfer knowledge from different
intermediate layers of the teacher model without differentiating their correctness. However …

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 …

Abstract Knowledge Distillation (KD) is extensively used in Natural Language Processing to
compress the pre-training and task-specific fine-tuning phases of large neural language …

Abstract Knowledge distillation (KD) aims to build a lightweight deep neural network model
under the guidance of a large-scale teacher model for model simplicity. Despite improved …

Unlike existing knowledge distillation methods focus on the baseline settings, where the
teacher models and training strategies are not that strong and competing as state-of-the-art …

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 …