  Diagnosing and cleaning data is a crucial step for building robust machine
learning systems. However, identifying problems within large-scale datasets
with real-world distributions is challenging due to the presence of complex
issues such as label errors, under-representation, and outliers. In this paper,
we propose a unified approach for identifying the problematic data by utilizing
a largely ignored source of information: a relational structure of data in the
feature-embedded space. To this end, we present scalable and effective
algorithms for detecting label errors and outlier data based on the relational
graph structure of data. We further introduce a visualization tool that
provides contextual information of a data point in the feature-embedded space,
serving as an effective tool for interactively diagnosing data. We evaluate the
label error and outlier/out-of-distribution (OOD) detection performances of our
approach on the large-scale image, speech, and language domain tasks, including
ImageNet, ESC-50, and SST2. Our approach achieves state-of-the-art detection
performance on all tasks considered and demonstrates its effectiveness in
debugging large-scale real-world datasets across various domains. We release
codes at https://github.com/snu-mllab/Neural-Relation-Graph.
