Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches involve leveraging appropriate metrics to minimize the divergence or distance between the knowledge extracted from the teacher model and the knowledge learned by the student model. Centered Kernel Alignment (CKA) is widely used to measure representation similarity and has been applied in several knowledge distillation methods. However, these methods are complex and fail to uncover the essence of CKA, thus not answering the question of how to use CKA to achieve simple and effective distillation properly. This paper first provides a theoretical perspective to illustrate the effectiveness of CKA, which decouples CKA to the upper bound of Maximum Mean Discrepancy~(MMD) and a constant term. Drawing from this, we propose a novel Relation-Centered Kernel Alignment~(RCKA) framework, which practically establishes a connection between CKA and MMD. Furthermore, we dynamically customize the application of CKA based on the characteristics of each task, with less computational source yet comparable performance than the previous methods. The extensive experiments on the CIFAR-100, ImageNet-1k, and MS-COCO demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs for image classification and object detection, validating the effectiveness of our approaches.

使用CKA（Centered Kernel Alignment）理论有效地将大规模模型和轻量级模型之间的表达差异解耦为MMD（Maximum Mean Discrepancy）的上界和一个常数项，提出了一种新颖的RCKA（Relation-Centered Kernel Alignment）框架，根据任务特征动态定制CKA的应用，以较低的计算源消耗获得相比前期方法相当的性能，在CIFAR-100、ImageNet-1k和MS-COCO上的广泛实验表明，该方法在几乎所有的师生网络对中对于图像分类和目标检测都达到了最先进的性能。

知识蒸馏中重新思考聚焦核对齐