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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10854681/ |
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