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 …

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 …

Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …

In this paper, we present Auto-KD, the first automated search framework for optimal
knowledge distillation design. Traditional distillation techniques typically require handcrafted …

Abstract Knowledge distillation (KD) has emerged as an effective technique for compressing
models that can enhance the lightweight model. Conventional KD methods propose various …

Abstract Knowledge distillation can be generally divided into offline and online categories
according to whether teacher model is pre-trained and persistent during the distillation …

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved
great success in learning visual representations without data annotations. However, most of …

Abstract Knowledge distillation (KD) transfers the dark knowledge from cumbersome
networks (teacher) to lightweight (student) networks and expects the student to achieve …

Knowledge distillation is initially introduced to utilize additional supervision from a single
teacher model for the student model training. To boost the student performance, some recent …

Due to the swee** digitalization of processes, increasingly vast amounts of time series
data are being produced. Accurate classification of such time series facilitates decision …