Keyang Cheng

Uncertainty Generation Meta-training for Cross-domain Few-shot Learning

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 transfer more effectively than existing techniques.