D4C：提升负样本质量以增强机器抽象推理能力
D4C: Improving Negative Example Quality to Enhance Machine Abstract Reasoning Ability
AI总结：
本文提出新型推理引擎Lico-Net及其定制版解决RPM与Bongard-Logo问题，针对负样本质量不足导致推理偏差，提出互为负样本结构与逐步线性注意力生成器两种方法，显著提升模型抽象推理能力。
摘要：
本文致力于解决提升人工智能抽象推理能力的挑战，特别是涉及复杂人类概念的任务。我们引入了 Lico-Net，一种基于深度学习理论的新型推理引擎，它将 Raven's Progressive Matrices (RPM) 问题的逻辑结构编码为概率表示。Lico-Net 在解决 RPM 任务方面表现出色。此外，我们提出了 Lico-Net-Bongard，这是专为 Bongard-Logo 问题定制的 Lico-Net 版本，它同样通过概率表示实现了高推理准确率。然而，我们观察到深度学习算法与人类在归纳推理概念的方式上存在不匹配，这主要归因于负样本质量不足。负样本配置不当会向深度学习算法传递错误的概念信息，从而扭曲其学习目标。为了解决这一问题，我们提出了两种新方法：首先，将推理问题中的不同样本点视为互为负样本，以改变数据中现有的负样本结构；其次，设计一种基于逐步线性注意力机制的负样本生成器，以产生高质量负样本。实验结果表明，这些方法显著提升了 Lico-Net (-Bongard) 及其他基线模型在 RPM 和 Bongard-Logo 数据集上的性能，以及在基础视觉模型处理领域的表现，特别是在解决 NICO 数据集的分布偏移问题时。我们的发现强调了提升负样本质量对于增强深度学习算法抽象推理能力的重要性，并表明此类系统代表了该领域未来研究的一个有前景的方向。
英文摘要：
This paper is dedicated to addressing the challenge of enhancing the abstract reasoning capabilities of AI, particularly for tasks involving complex human concepts. We introduce Lico-Net, a novel reasoning engine grounded in deep learning theory, which encodes the logical structure of Raven's Progressive Matrices (RPM) problems into probabilistic representations. Lico-Net excels in solving RPM tasks. Furthermore, we propose Lico-Net-Bongard, a tailored version of Lico-Net for the Bongard-Logo problem, which also achieves high reasoning accuracy through probabilistic representations. However, we observe a mismatch between the way deep learning algorithms and humans induce reasoning concepts, primarily attributed to the inadequate quality of negative samples. Improper configuration of negative samples can convey erroneous conceptual information to deep learning algorithms, thereby distorting their learning objectives. To address this issue, we propose two novel approaches: first, treating different sample points within reasoning problems as mutual negative samples to alter the existing negative sample structure in the data; second, designing a negative sample generator based on a step-wise linear attention mechanism to produce high-quality negative samples. Experimental results demonstrate that these methods significantly improve the performance of Lico-Net (-Bongard) and other baseline models on the RPM and Bongard-Logo datasets, as well as in the domain of foundational vision model processing, particularly when addressing the NICO dataset's distribution shift problem. Our findings emphasize the importance of improving negative sample quality for enhancing the abstract reasoning capabilities of deep learning algorithms and suggest that systems represent a promising direction for future research in this field.
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。
