Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

Abstract Knowledge distillation transfers knowledge from the teacher network to the student
one, with the goal of greatly improving the performance of the student network. Previous …

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 …

State-of-the-art distillation methods are mainly based on distilling deep features from
intermediate layers, while the significance of logit distillation is greatly overlooked. To …

Abstract Knowledge distillation (KD) achieves promising results on the challenging problem
of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in …

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 …

Most existing distillation methods ignore the flexible role of the temperature in the loss
function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In …

This article addresses the problem of distilling knowledge from a large teacher model to a
slim student network for LiDAR semantic segmentation. Directly employing previous …

Abstract Knowledge Distillation (KD) aims at distilling the knowledge from the large teacher
model to a lightweight student model. Mainstream KD methods can be divided into two …

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 …