We introduce a new neural architecture for solving visual abstract reasoning tasks inspired by human cognition, specifically by observations that human abstract reasoning often interleaves perceptual and conceptual processing as part of a flexible, iterative, and dynamic cognitive process. Inspired by this principle, our architecture models visual abstract reasoning as an iterative, self-contrasting learning process that pursues consistency between perceptual and conceptual processing of visual stimuli. We explain how this new Contrastive Perceptual-Conceptual Network (CPCNet) works using matrix reasoning problems in the style of the well-known Raven's Progressive Matrices intelligence test. Experiments on the machine learning dataset RAVEN show that CPCNet achieves higher accuracy than all previously published models while also using the weakest inductive bias. We also point out a substantial and previously unremarked class imbalance in the original RAVEN dataset, and we propose a new variant of RAVEN -- AB-RAVEN -- that is more balanced in terms of abstract concepts.

翻译：我们提出了一种解决视觉抽象推理任务的新型神经架构，该架构受人类认知启发——尤其是观察到人类抽象推理常将感知处理与概念处理交织进行，作为灵活、迭代且动态的认知过程的一部分。基于这一原理，我们的架构将视觉抽象推理建模为一种迭代的自我对比学习过程，追求视觉刺激的感知处理与概念处理之间的一致性。我们通过著名的瑞文渐进矩阵智力测试风格的矩阵推理问题，阐释了这种新的对比感知-概念网络（CPCNet）的工作原理。在机器学习数据集RAVEN上的实验表明，CPCNet在采用最弱归纳偏置的同时，达到了比所有先前已发表模型更高的准确率。我们还指出了原始RAVEN数据集中一个此前未被注意的严重类别不平衡问题，并提出了RAVEN的新变体——AB-RAVEN——该变体在抽象概念上更为均衡。