scholar.google.com › citations
To address this issue, we propose a method for designing the search space with step by step and analyze the trends of graphs to design graphs with high accuracy ...
Abstract—Knowledge distillation is one of the most widely utilized methods to improve the performance of a model. The knowledge transfer graph has been ...
To address this issue, we propose a method for designing the search space with step by step and analyze the trends of graphs to design graphs with high accuracy ...
Sachi Iwata, Soma Minami, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi: Refining Design Spaces in Knowledge Distillation for Deep Collaborative ...
A novel graph representation called knowledge transfer graph is proposed that provides a unified view of the knowledge transfer and has the potential to ...
People also ask
What is knowledge distillation in deep learning?
What is online knowledge distillation?
Online Knowledge Distillation via Collaborative Learning. ... Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.
The knowledge transfer graph provides a unified view of KD, and has the potential to represent diverse knowledge patterns. ... ... A graphical illustration of ...
Specif- ically, we carefully design multiple methods to generate soft target as supervisions by effectively ensembling pre- dictions of students and distorting ...
Missing: Spaces | Show results with:Spaces
Dec 26, 2023 · Cloud-Device Collaborative Learning. Merely of- floading the ... Knowledge Distillation (KD) is a method of model com- pression and ...
Ensemble knowledge distillation for learning improved and efficient networks. Asif, U., Tang, J. & Harrer, S. (2020). ECAI. Do deep nets really need to be deep?