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IEEE/ICACT20260373 Slide.13        [Big Slide]       [YouTube]	Oral Presentation
	To tackle the risk of overfitting inherent in small datasets, we implemented specific regularization techniques. We used Asymmetric Dropout, applying a 20% rate to the Generator to reduce co-adaptation and a higher 40% rate to the Discriminator to suppress sensitivity to local noise, alongside L2 Weight Decay in both networks to explicitly constrain the energy distribution of the weight vectors.

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