Displacement Knowledge Distillation matches the distribution of feature differences between teacher and student, and a dual distillation network applies it to novel classes in few-shot class-incremental learning, achieving state-of-the-art retention on CIFAR-100, miniImageNet, and CUB-200.
Few- shot class-incremental learning via relation knowledge distillation,
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On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
Displacement Knowledge Distillation matches the distribution of feature differences between teacher and student, and a dual distillation network applies it to novel classes in few-shot class-incremental learning, achieving state-of-the-art retention on CIFAR-100, miniImageNet, and CUB-200.