Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI)
has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) …

The high-speed mobile networks offer great potentials to many future intelligent applications,
such as autonomous vehicles in smart transportation systems. Such networks provide the …

Road hazards (RH) have always been the cause of many serious traffic accidents. These
have posed a threat to the safety of drivers, passengers, and pedestrians, and have also …

With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple
student models collaboratively and distill knowledge from each other. Although existing …

Abstract Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher
network to strengthen a smaller student. Existing methods focus on excavating the …

Abstract Online Knowledge Distillation (OKD) is designed to alleviate the dilemma that the
high-capacity pre-trained teacher model is not available. However, the existing methods …

At present, the method based on deep learning performs well in public object detection
tasks. However, there are still two problems to be solved for industrial defect detection: 1) …

Neurologically, filter pruning is a procedure of forgetting and remembering recovering.
Prevailing methods directly forget less important information from an unrobust baseline at …

Deep learning models have been widely used in pathological image classification.
However, most researches employ complex but inefficient neural networks to implement this …