用神经嵌入和向量算术求解ARC视觉类比：一种通用方法
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method
AI总结：
本研究将词嵌入的向量算术机制推广到视觉类比，用VAE将ARC项编码为潜在向量并通过向量运算求解，在简单项上效果良好，官方ARC得分2%，ConceptARC得分8.8%，方法可推广至其他抽象视觉推理任务。
摘要：
类比推理从已知关系中提取信息，并将这些信息推广到相似但陌生的情况。深度学习模型最早能够解决语言类比的通用方式之一是通过词嵌入的向量算术，本质上关联映射到向量空间中的词（例如，国王-男人+女人=？）。相比之下，大多数解决视觉类比的尝试仍然主要是任务特定的，通用性较差。本项目聚焦于视觉类比推理，并将最初用于解决语言类比的通用机制应用到视觉领域。以抽象推理语料库（ARC）为例来研究视觉类比求解，我们使用变分自编码器（VAE）将ARC项转换为低维潜在向量，类似于语言方法中使用的词嵌入。通过简单的向量算术，发现并利用ARC项的底层规则来求解它们。结果表明，该方法在维度较少（即使用的颜色少、形状均匀）、输入输出示例相似以及VAE重建精度高的简单项上效果良好。对更复杂项的预测与预期输出偏差较大，尽管预测仍经常接近项规则集的部分内容。错误模式表明模型按预期工作。在官方ARC基准上，模型得分为2%（而当前世界纪录为21%），在ConceptARC上得分为8.8%。尽管所提出的方法涉及基本的降维技术和标准向量算术，但该方法在ARC上显示出有前景的结果，并且可以轻松推广到其他抽象视觉推理任务。
英文摘要：
Analogical reasoning derives information from known relations and generalizes this information to similar yet unfamiliar situations. One of the first generalized ways in which deep learning models were able to solve verbal analogies was through vector arithmetic of word embeddings, essentially relating words that were mapped to a vector space (e.g., king - man + woman = __?). In comparison, most attempts to solve visual analogies are still predominantly task-specific and less generalizable. This project focuses on visual analogical reasoning and applies the initial generalized mechanism used to solve verbal analogies to the visual realm. Taking the Abstraction and Reasoning Corpus (ARC) as an example to investigate visual analogy solving, we use a variational autoencoder (VAE) to transform ARC items into low-dimensional latent vectors, analogous to the word embeddings used in the verbal approaches. Through simple vector arithmetic, underlying rules of ARC items are discovered and used to solve them. Results indicate that the approach works well on simple items with fewer dimensions (i.e., few colors used, uniform shapes), similar input-to-output examples, and high reconstruction accuracy on the VAE. Predictions on more complex items showed stronger deviations from expected outputs, although, predictions still often approximated parts of the item's rule set. Error patterns indicated that the model works as intended. On the official ARC paradigm, the model achieved a score of 2% (cf. current world record is 21%) and on ConceptARC it scored 8.8%. Although the methodology proposed involves basic dimensionality reduction techniques and standard vector arithmetic, this approach demonstrates promising outcomes on ARC and can easily be generalized to other abstract visual reasoning tasks.
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。
