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arXiv 2201.00058cs.LGcs.AImath.ATmath.KTmath.SG
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表示拓扑散度:一种用于比较神经网络表示的方法

Representation Topology Divergence: A Method for Comparing Neural Network Representations

Skolkovo Institute of Science and Technology(斯科尔科沃科学技术研究院);CNRS(法国国家科学研究中心);Université Paris Cité(巴黎西岱大学);Artificial Intelligence Research Institute (AIRI)(人工智能研究院(AIRI))

Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev

AI总结:

本研究提出表示拓扑散度RTD,通过多尺度拓扑衡量等大点云的表示差异,适用于真实机器学习数据集,可用于分析计算机视觉与NLP领域的神经网络表示相关问题。

摘要:

数据表示比较是一个复杂的多方面问题,目前尚未有完整的解决方案。我们提出了一种用于比较两种数据表示的方法。我们引入了表示拓扑散度(Representation Topology Divergence,RTD),用于衡量两个大小相等且点之间存在一一对应关系的点云在多尺度拓扑上的差异。这些数据点云可以位于不同的环境空间中。RTD是少数几种适用于真实机器学习数据集的基于拓扑数据分析(TDA)的实用方法之一。实验表明,所提出的RTD与数据表示相似性的直观评估一致,并且对其拓扑结构敏感。我们应用RTD来深入了解计算机视觉和NLP领域中神经网络针对各类问题的表示:训练动力学分析、数据分布偏移、迁移学习、集成学习、解耦评估。

英文摘要:

Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet. We propose a method for comparing two data representations. We introduce the Representation Topology Divergence (RTD), measuring the dissimilarity in multi-scale topology between two point clouds of equal size with a one-to-one correspondence between points. The data point clouds are allowed to lie in different ambient spaces. The RTD is one of the few TDA-based practical methods applicable to real machine learning datasets. Experiments show that the proposed RTD agrees with the intuitive assessment of data representation similarity and is sensitive to its topological structure. We apply RTD to gain insights on neural networks representations in computer vision and NLP domains for various problems: training dynamics analysis, data distribution shift, transfer learning, ensemble learning, disentanglement assessment.

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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