In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM). To discover compositional structures of the data, we propose the Scattering Compositional Learner (SCL), an architecture that composes neural networks in a sequence. Our SCL achieves state-of-the-art performance on two RPM datasets, with a 48.7% relative improvement on Balanced-RAVEN and 26.4% on PGM over the previous state-of-the-art. We additionally show that our model discovers compositional representations of objects' attributes (e.g., shape color, size), and their relationships (e.g., progression, union). We also find that the compositional representation makes the SCL significantly more robust to test-time domain shifts and greatly improves zero-shot generalization to previously unseen analogies.

本文介绍了一种用于寻找仿生推理任务中数据复合结构的方法并提出了一种通过神经网络序列组合的算法，称为散射复合学习器(SCL)。研究发现该算法在两个数据集上取得了最新成果，并改善了物体属性关系的组合表达式。此外，该算法具有更好的鲁棒性和更高的泛化能力。

散射组合学习器：在类比推理中发现对象、属性和关系