arXiv 2103.005192021-03cs.AI

KANDINSKYPatterns --模式分析与机器智能的实验探索环境

KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence

Medical University Graz（格拉茨医科大学）

Andreas Holzinger, Anna Saranti, Heimo Mueller

备注：: 12 pages, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), currently under review

摘要：

当具有高质量的训练数据时，机器智能在标准识别任务中非常成功。机器级的模式识别和人类级的概念学习之间仍然存在显著的差距。人类可以在不确定的情况下仅从几个例子中学习，并将这些概念推广到解决新问题。对可解释的机器智能的兴趣日益增长，需要实验环境和诊断测试来分析现有方法的弱点，以推动该领域的进展。在本文中，我们讨论了现有的诊断测试和测试数据集，如CLEVR，CLEVERER，CLOSURE，CURI，Bongard-Paddle，V-PROM，并提出了我们自己的实验环境：KANDINSKYPatterns，命名的俄罗斯艺术家瓦西里·康定斯基，谁作出了理论贡献的组成，即所有的感知组成的几何基本的个人组成部分。这一点在20世纪60年代被Hubel & Wibel实验证明，并成为机器学习方法的基础，如Neocognitron和更晚的深度学习。虽然KANDINSKYPatterns一方面具有计算可控的属性，带来了地面真理，但它们也很容易被人类观察者区分，即，受控模式可以由人类和算法描述，使其成为国际机器智能研究的另一个重要贡献。

英文摘要：

Machine intelligence is very successful at standard recognition tasks when having high-quality training data. There is still a significant gap between machine-level pattern recognition and human-level concept learning. Humans can learn under uncertainty from only a few examples and generalize these concepts to solve new problems. The growing interest in explainable machine intelligence, requires experimental environments and diagnostic tests to analyze weaknesses in existing approaches to drive progress in the field. In this paper, we discuss existing diagnostic tests and test data sets such as CLEVR, CLEVERER, CLOSURE, CURI, Bongard-LOGO, V-PROM, and present our own experimental environment: The KANDINSKYPatterns, named after the Russian artist Wassily Kandinksy, who made theoretical contributions to compositivity, i.e. that all perceptions consist of geometrically elementary individual components. This was experimentally proven by Hubel &Wiesel in the 1960s and became the basis for machine learning approaches such as the Neocognitron and the even later Deep Learning. While KANDINSKYPatterns have computationally controllable properties on the one hand, bringing ground truth, they are also easily distinguishable by human observers, i.e., controlled patterns can be described by both humans and algorithms, making them another important contribution to international research in machine intelligence.

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。

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