Euclidean Norm Regularization is a regularization step used in generative adversarial networks, and is typically added to both the generator and discriminator losses:
$$ R_{z} = w_{r} \cdot ||\Delta{z}||^{2}_{2} $$
where the scalar weight $w_{r}$ is a parameter.
Image: LOGAN
Source: Deep Compressed SensingPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Bias Detection | 2 | 15.38% |
Clustering | 2 | 15.38% |
Fairness | 1 | 7.69% |
Edge-computing | 1 | 7.69% |
Denoising | 1 | 7.69% |
Decision Making | 1 | 7.69% |
BIG-bench Machine Learning | 1 | 7.69% |
Computational Efficiency | 1 | 7.69% |
Conditional Image Generation | 1 | 7.69% |
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