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 …

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

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 …

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 …

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 …

Abstract Knowledge distillation has been applied to image classification successfully.
However, object detection is much more sophisticated and most knowledge distillation …

Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by
leveraging different forms of weak supervision. This helps generalize to novel objects at …

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 …

Abstract Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from individual …