Knowledge distillation (KD) is one of the most useful techniques for light-weight neural networks. Although neural networks have a clear purpose of embedding datasets into the low-dimensional space, the existing knowledge was quite far from this purpose and provided only limited information. We argue that good knowledge should be able to interpret the embedding procedure. This paper proposes a method of generating interpretable embedding procedure (IEP) knowledge based on principal component analysis, and distilling it based on a message passing neural network. Experimental results show that the student network trained by the proposed KD method improves 2.28% in the CIFAR100 dataset, which is higher performance than the state-of-the-art (SOTA) method. We also demonstrate that the embedding procedure knowledge is interpretable via visualization of the proposed KD process. The implemented code is available at https://github.com/sseung0703/IEPKT.

本文提出一种基于主成分分析生成可解释嵌入过程知识的知识蒸馏方法，用于训练轻量级的神经网络。实验结果表明，该方法在CIFAR100数据集上提高了2.28%的性能，而且嵌入过程的知识可以通过可视化KD过程来解释。

可解释化嵌入过程的知识转移：基于层叠主成分分析和图神经网络