This paper aims to analyze knowledge consistency between pre-trained deep
neural networks. We propose a generic definition for knowledge consistency
between neural networks at different fuzziness levels. A task-agnostic method
is designed to disentangle feature components, which represent the consistent
knowledge, from raw intermediate-layer features of each neural network. As a
generic tool, our method can be broadly used for different applications. In
preliminary experiments, we have used knowledge consistency as a tool to
diagnose representations of neural networks. Knowledge consistency provides new
insights to explain the success of existing deep-learning techniques, such as
knowledge distillation and network compression. More crucially, knowledge
consistency can also be used to refine pre-trained networks and boost
performance.

本文旨在分析预训练深度神经网络之间的知识一致性，并提出了一个泛化定义。方式是设计了一种任务不可知的方法来解开每个神经网络的原始中间层特征和表示一致知识的特征组成部分。这项工作可以用于不同应用，例如调整预训练网络以提高性能并解释基于知识蒸馏和网络压缩等深度学习技术成功的原因。