Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization

Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization

In the domain of Deep Neural Networks (DNNs), the deployment of regularization techniques is a common strategy for optimizing network performance. While these methods have been shown to be effective for optimization, they typically necessitate complete retraining of the network. We propose a trainin...

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Bibliographic Details
Main Authors: Naim Reza, Ho Yub Jung
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Regularization
gradient regularization
re-calibration
deep neural network
Online Access:https://ieeexplore.ieee.org/document/10854681/
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https://ieeexplore.ieee.org/document/10854681/

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