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 …

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an
image. This task is challenging due to multi-scale body parts, fine-grained localization for …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

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

Abstract Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to
guide the student, while self-KD does not need a real teacher to require the soft labels. This …

The representation gap between teacher and student is an emerging topic in knowledge
distillation (KD). To reduce the gap and improve the performance, current methods often …

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

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale
problems in many areas, such DNNs are hard to be deployed in real-world systems due to …