In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance. Finally, we show that a simple soft maximum function can be used to address any significant capacity gap problems. Experimental results on various benchmark datasets demonstrate that using these insights can lead to superior or comparable performance to state-of-the-art knowledge distillation techniques, despite being much more computationally efficient. In particular, we obtain these results across image classification (CIFAR100 and ImageNet), object detection (COCO2017), and on more difficult distillation objectives, such as training data efficient transformers, whereby we attain a 77.2% top-1 accuracy with DeiT-Ti on ImageNet.

翻译：本文重新审视了知识蒸馏作为函数匹配与度量学习问题的有效性。我们验证了三个关键设计要素：归一化、软最大值函数和投影层。理论上证明投影器隐式编码了过往样本信息，为学生模型提供了关系梯度。随后表明表征归一化与投影器训练动态紧密耦合，对学生模型性能产生重大影响。最后证明使用简单软最大值函数可有效解决显著容量差距问题。在多个基准数据集上的实验结果表明，尽管计算效率显著提升，基于上述洞察的方法在图像分类（CIFAR100和ImageNet）、目标检测（COCO2017）及更复杂的蒸馏任务（如训练数据高效Transformer）中，均能达到或超越现有最优知识蒸馏技术的性能。特别地，在ImageNet上使用DeiT-Ti取得了77.2%的top-1准确率。