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A lightweight, input-conditioned initialization method boosts CNN performance without additional training costs, achieving significant accuracy gains across multiple datasets.
DREG not only achieves superior accuracy but also thrives in data-scarce environments, making it a game-changer for deep learning regularization strategies.
