类内省：一种利用分类器可解释性方法检测未标记子类的新技术
Class Introspection: A Novel Technique for Detecting Unlabeled Subclasses by Leveraging Classifier Explainability Methods
摘要：
检测数据集内的潜在结构是执行数据集分析的关键步骤。然而，现有的最先进的子类发现技术是有限的：要么他们是有限的检测非常少量的离群值，或者他们缺乏统计能力来处理复杂的数据，如图像或音频。本文提出了一种解决这个子类发现问题的方法：通过利用实例解释方法，现有的分类器可以扩展到通过分类器的内部决策对每个实例的差异来检测潜在类。这不仅适用于简单的分类技术，也适用于深度神经网络，从而提供了一种强大而灵活的方法来检测数据集中的潜在结构。实际上，这代表了数据集到分类器的“解释空间”的投影，初步结果表明，即使在有限的处理下，这种技术也优于潜在类检测的基线。本文还包含一个自动分析分类器的管道，以及一个用于交互式探索该技术结果的Web应用程序。
英文摘要：
Detecting latent structure within a dataset is a crucial step in performing analysis of a dataset. However, existing state-of-the-art techniques for subclass discovery are limited: either they are limited to detecting very small numbers of outliers or they lack the statistical power to deal with complex data such as image or audio. This paper proposes a solution to this subclass discovery problem: by leveraging instance explanation methods, an existing classifier can be extended to detect latent classes via differences in the classifier's internal decisions about each instance. This works not only with simple classification techniques but also with deep neural networks, allowing for a powerful and flexible approach to detecting latent structure within datasets. Effectively, this represents a projection of the dataset into the classifier's "explanation space," and preliminary results show that this technique outperforms the baseline for the detection of latent classes even with limited processing. This paper also contains a pipeline for analyzing classifiers automatically, and a web application for interactively exploring the results from this technique.
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。
