This article discusses a new approach to knowledge distillation for improving the recognition accuracy of lightweight models. By using a cross-stage feature fusion symmetric…
Browsing: Knowledge Distillation
This article explores the potential of compact language models, which are smaller versions of large language models that offer scalability, accessibility, and efficiency to…
ReffAKD is a novel approach for knowledge distillation that uses autoencoders to generate high-quality soft labels without relying on a large teacher model or…
TinyML is a sub-branch of machine learning that focuses on lightweight algorithms capable of running on a device, rather than on a server, with…
This article discusses TinyML, a sub-branch of machine learning that is concerned with lightweight algorithms capable of running on a device, rather than on…
This article discusses the use of lateral cephalogram in orthodontics as a valuable screening tool for the diagnosis of obstructive sleep apnea (OSA). A…
Knowledge distillation is a machine learning compression process that transfers knowledge from a large deep learning model to a smaller, more efficient model. The…