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Black-box knowledge distillation can be significantly improved by synthesizing diverse image priors and using contrastive learning to enhance the distinctions between synthetic samples.
Augmenting few-shot knowledge distillation with adaptively selected, teacher-confident GAN-generated images dramatically boosts student accuracy.
