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 …

Knowledge distillation (KD) is a prevalent model compression technique in deep learning,
aiming to leverage knowledge from a large teacher model to enhance the training of a …

Most researchers focus on designing accurate crowd counting models with heavy
parameters and computations but ignore the resource burden during the model deployment …

Knowledge distillation (KD) enhances the performance of a student network by allowing it to
learn the knowledge transferred from a teacher network incrementally. Existing methods …

Knowledge Distillation (KD) has proven effective for compressing large teacher models into
smaller student models. While it is well known that student models can achieve similar …

Recent object re-identification (Re-ID) methods gain high efficiency via lightweight student
models trained by knowledge distillation (KD). However, the huge architectural difference …

Existing knowledge distillation (KD) methods are mainly based on features, logic, or
attention, where features and logic represent the results of reasoning at different stages of a …

To apply the latest computer vision techniques that require a large computational cost in real
industrial applications, knowledge distillation methods (KDs) are essential. Existing logit …

Knowledge distillation, a widely adopted model compression technique, distils knowledge
from a large teacher model to a smaller student model, with the goal of reducing the …

Few-shot classification (FSC) is a challenging task due to limitation in accessing training
data. Recent methods often employ highly complex networks to obtain high-quality features …