Sen Huang

Adversarial contrastive training allows for effective audio classification across domain shifts, outperforming existing methods in few-shot scenarios.

FOAC achieves superior audio classification performance while maintaining lower computational complexity, setting a new benchmark for few-shot learning in open-set scenarios.

Class-variable audio classification can adapt to both increasing and decreasing classes, achieving superior accuracy through innovative prototype adaptation and training strategies.