Machine learning models that achieve high overall accuracy often make
systematic errors on important subsets (or slices) of data. Identifying
underperforming slices is particularly challenging when working with
high-dimensional inputs (e.g. images, audio), where important slices are often
unlabeled. In order to address this issue, recent studies have proposed
automated slice discovery methods (SDMs), which leverage learned model
representations to mine input data for slices on which a model performs poorly.
To be useful to a practitioner, these methods must identify slices that are
both underperforming and coherent (i.e. united by a human-understandable
concept). However, no quantitative evaluation framework currently exists for
rigorously assessing SDMs with respect to these criteria. Additionally, prior
qualitative evaluations have shown that SDMs often identify slices that are
incoherent. In this work, we address these challenges by first designing a
principled evaluation framework that enables a quantitative comparison of SDMs
across 1,235 slice discovery settings in three input domains (natural images,
medical images, and time-series data). Then, motivated by the recent
development of powerful cross-modal representation learning approaches, we
present Domino, an SDM that leverages cross-modal embeddings and a novel
error-aware mixture model to discover and describe coherent slices. We find
that Domino accurately identifies 36% of the 1,235 slices in our framework - a
12 percentage point improvement over prior methods. Further, Domino is the
first SDM that can provide natural language descriptions of identified slices,
correctly generating the exact name of the slice in 35% of settings.

本文介绍了面向高维输入的自动 SLICE 发现方法，以便于对机器学习模型进行更有针对性的优化，通过设计了一个基于数量的评估框架，发现了命名为 Domino 的自动 SLICE 发现方法，在三个输入领域中能够发现出大约 36% 的 SLICE, 这比以前的方法有了很大的改进，而且 Domino 还可以提供自然语言的 SLICE 描述。