一种基于认知启发的神经架构：利用对比感知与概念处理进行视觉抽象推理
A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing
AI总结：
受人类认知启发，提出一种将视觉抽象推理建模为迭代自对比学习过程的 CPCNet 架构，在 RAVEN 数据集上以最弱归纳偏置实现最高准确率，并提出了类别更平衡的 AB-RAVEN 变体。
摘要：
我们引入了一种新的神经架构，用于解决视觉抽象推理任务，其灵感来源于人类认知，特别是基于人类抽象推理常将感知和概念处理交织在一起，作为灵活、迭代和动态认知过程的一部分这一观察。受此原则启发，我们的架构将视觉抽象推理建模为迭代的自对比学习过程，追求对视觉刺激的感知和概念处理之间的一致性。我们以著名 Raven 渐进矩阵智力测试风格的矩阵推理问题为例，解释了这种新的对比感知-概念网络（Contrastive Perceptual-Conceptual Network, CPCNet）的工作原理。在机器学习数据集 RAVEN 上的实验表明，CPCNet 比所有先前发表的模型实现了更高的准确率，同时使用了最弱的归纳偏置。我们还指出了原始 RAVEN 数据集中存在的一个重大且此前未被注意的类别不平衡问题，并提出了一种新的 RAVEN 变体——AB-RAVEN，它在抽象概念方面更加平衡。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
