Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We
have created various computer-mediated virtual environments, including social networks …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …

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 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 …

Despite the visually-pleasing results achieved, the massive computational cost has been a
long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits …

Abstract Knowledge distillation has been applied to various tasks successfully. The current
distillation algorithm usually improves students' performance by imitating the output of the …