来自今天的爱可可AI前沿推介

[CV] Pearl Causal Hierarchy on Image Data: Intricacies & Challenges

M Zečević, M Willig, D S Dhami, K Kersting
[TU Darmstadt]

图像数据上的Pearl因果层次：复杂性和挑战

要点:

  1. 研究人员表示支持Pearl因果反事实理论说作为AI/ML研究的基石；
  2. 像缺乏基础事实标准和经典问题统一视角这样的主要挑战阻碍了研究进程的发展；
  3. 本文证明了Pearl因果层次在图像数据上如何得到解释。

摘要：
许多研究人员表示支持因果关系的Pearl反事实理论，将其作为AI/ML研究智能系统最终目标的垫脚石。与其他所有不断增长的子领域一样，耐心似乎是一种美德，因为在整合这两个领域的概念方面取得重大进展需要时间，然而，缺乏基本事实基准或对计算机视觉等经典问题的统一视角等重大挑战似乎阻碍了研究运动的势头。本文例证了如何通过提供对几个复杂之处的见解，以及将Pearl因果关系的关键概念应用于图像数据研究时自然出现的挑战，如何在图像数据上理解Pearl因果层次结构(PCH)。

Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in any other growing subfield, patience seems to be a virtue since significant progress on integrating notions from both fields takes time, yet, major challenges such as the lack of ground truth benchmarks or a unified perspective on classical problems such as computer vision seem to hinder the momentum of the research movement. This present work exemplifies how the Pearl Causal Hierarchy (PCH) can be understood on image data by providing insights on several intricacies but also challenges that naturally arise when applying key concepts from Pearlian causality to the study of image data.

论文链接：https://arxiv.org/abs/2212.12570
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