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An innovative meta-learning framework augmented by uncertainty generation is proposed that improves the current state-of-the-art by an average of 3.11%, with key innovations in using probabilistic feature statistics, gradient-based uncertainty modeling, and causal feature extraction that enhance cross-domain knowledge...
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