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Diagnosing and cleaning data is a crucial step for building robust machinelearning systems. However, identifying problems within large-scale datasetswith real-world distributions is challenging due to the presence of complexissues such as label errors, under-representation, and outliers. In this paper,we propose a unified approach for identifying the problematic data by utilizinga largely ignored source of information: a relational structure of data in thefeature-embedded space. To this end, we present scalable and effectivealgorithms for detecting label errors and outlier data based on the relationalgraph structure of data. We further introduce a visualization tool thatprovides contextual information of a data point in the feature-embedded space,serving as an effective tool for interactively diagnosing data. We evaluate thelabel error and outlier/out-of-distribution (OOD) detection performances of ourapproach on the large-scale image, speech, and language domain tasks, includingImageNet, ESC-50, and SST2. Our approach achieves state-of-the-art detectionperformance on all tasks considered and demonstrates its effectiveness indebugging large-scale real-world datasets across various domains. We releasecodes at https://github.com/snu-mllab/Neural-Relation-Graph.

Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data