This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation. Up to now, although the definition of knowledge encoded by the DNN has not reached a consensus, Previous studies have derived a series of mathematical evidence to take interactions as symbolic primitive inference patterns encoded by a DNN. We extend the definition of interactions and, for the first time, extract interactions encoded by intermediate layers. We quantify and track the newly emerged interactions and the forgotten interactions in each layer during the forward propagation, which shed new light on the learning behavior of DNNs. The layer-wise change of interactions also reveals the change of the generalization capacity and instability of feature representations of a DNN.

翻译：本文旨在解释深度神经网络在前向传播过程中如何逐层提取新知识并遗忘噪声特征。迄今为止，尽管对深度神经网络所编码知识的定义尚未达成共识，但先前研究已推导出一系列数学证据，将交互作用视为深度神经网络编码的符号化原始推理模式。我们扩展了交互作用的定义，并首次提取了中间层所编码的交互作用。我们量化并追踪了前向传播过程中每层新出现的交互作用及被遗忘的交互作用，这为理解深度神经网络的学习行为提供了新视角。交互作用的逐层变化也揭示了深度神经网络泛化能力与特征表示稳定性的动态演变。