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ReffAKD is a novel approach for knowledge distillation that uses autoencoders to generate high-quality soft labels without relying on a large teacher model or costly crowd-sourcing. This allows for the training of compact “student” models on devices with limited computing power, making it a potential solution for deploying deep neural networks on embedded systems or edge platforms.
