In this paper, we hypothesize that internal function sharing is one of the reasons to weaken o.o.d. or systematic generalization in deep learning for classification tasks. Under equivalent prediction, a model partitions an input space into multiple parts separated by boundaries. The function sharing prefers to reuse boundaries, leading to fewer parts for new outputs, which conflicts with systematic generalization. We show such phenomena in standard deep learning models, such as fully connected, convolutional, residual networks, LSTMs, and (Vision) Transformers. We hope this study provides novel insights into systematic generalization and forms a basis for new research directions.

翻译：在本文中,我们假设内部职能共享是削弱o.o.d.d.或系统化地概括为分类任务的深层学习的原因之一。在同等的预测下,模型将输入空间分隔为边界分开的多个部分。功能共享倾向于再利用边界,导致新产出的部件减少,这与系统化的概括相冲突。我们在标准深层次学习模型(如完全相连的、革命性的、残余的网络、LSTMS和(视觉的)变异器)中显示了这种现象。我们希望这项研究能够提供对系统化的概括性的新见解,并为新的研究方向奠定基础。