Optimizing Knowledge Transfer Graph for Deep Collaborative Learning

Optimizing Knowledge Transfer Graph for Deep Collaborative Learning

Knowledge transfer among multiple networks, using predicted probabilities or intermediate-layer activations, has evolved significantly through extensive manual design, ranging from simple teacher—student approaches (for example, knowledge distillation) to bidirectional cohort methods (for...

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Bibliographic Details
Main Authors: Soma Minami, Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Collaborative learning
knowledge distillation
hyperparameter search
AutoML
Online Access:https://ieeexplore.ieee.org/document/11031446/
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