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.

我们介绍了一种新的神经架构，用于解决视觉抽象推理任务，受到人类认知的启发，特别是人类抽象推理经常在感知和概念处理之间交替进行，作为一种灵活、迭代和动态的认知过程。我们介绍了如何使用矩阵推理问题来解释这种新的对比感知 - 概念网络（CPCNet）的工作方式，以形式化问题的思维。在机器学习数据集 RAVEN 上的实验证明，CPCNet 相比之前的所有模型具有更高的准确性，同时使用了最弱的归纳偏差。我们还指出了原始 RAVEN 数据集中的显著且以前未被注意到的类别不平衡问题，并提出了一个新的变体 RAVEN--AB-RAVEN，该变体在抽象概念方面更加平衡。

基于认知启发的神经结构在视觉抽象推理中运用对比感知和概念处理

A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning  Using Contrastive Perceptual and Conceptual Processing

While achieving unmatched performance on many well-defined tasks, deep
learning models have also been used to solve visual abstract reasoning tasks,
which are relatively less well-defined, and have been widely used to measure
human intelligence. However, current deep models struggle to match human
abilities to solve such tasks with minimum data but maximum generalization. One
limitation is that current deep learning models work in a monotonic way, i.e.,
treating different parts of the input in essentially fixed orderings, whereas
people repeatedly observe and reason about the different parts of the visual
stimuli until the reasoning process converges to a consistent conclusion, i.e.,
non-monotonic reasoning. This paper proposes a non-monotonic computational
approach to solve visual abstract reasoning tasks. In particular, we
implemented a deep learning model using this approach and tested it on the
RAVEN dataset -- a dataset inspired by the Raven's Progressive Matrices test.
Results show that the proposed approach is more effective than existing
monotonic deep learning models, under strict experimental settings that
represent a difficult variant of the RAVEN dataset problem.

本文提出了一种非单调计算方法来解决视觉抽象推理任务，使用 RAVEN 数据集进行测试，并与现有单调深度学习模型进行比较，结果表明，该方法比现有单调深度学习模型更加有效。

视觉抽象推理任务的深度非单调推理

Deep Non-Monotonic Reasoning for Visual Abstract Reasoning Tasks

Modern neural network architectures can leverage large amounts of data to
generalize well within the training distribution. However, they are less
capable of systematic generalization to data drawn from unseen but related
distributions, a feat that is hypothesized to require compositional reasoning
and reuse of knowledge. In this work, we present Neural Interpreters, an
architecture that factorizes inference in a self-attention network as a system
of modules, which we call \emph{functions}. Inputs to the model are routed
through a sequence of functions in a way that is end-to-end learned. The
proposed architecture can flexibly compose computation along width and depth,
and lends itself well to capacity extension after training. To demonstrate the
versatility of Neural Interpreters, we evaluate it in two distinct settings:
image classification and visual abstract reasoning on Raven Progressive
Matrices. In the former, we show that Neural Interpreters perform on par with
the vision transformer using fewer parameters, while being transferrable to a
new task in a sample efficient manner. In the latter, we find that Neural
Interpreters are competitive with respect to the state-of-the-art in terms of
systematic generalization

本研究提出了一种名为神经解析器（Neural Interpreters）的架构，它将自我关注网络中的推理分解为一组模块，可以在宽度和深度上灵活地组成计算，并可在训练后进行容量扩展。我们的实验表明，该架构在图像分类和视觉抽象推理方面表现出了很高的性能，并能够很好地支持系统化的泛化。