  In this paper, we present DendroMap, a novel approach to interactively
exploring large-scale image datasets for machine learning (ML). ML
practitioners often explore image datasets by generating a grid of images or
projecting high-dimensional representations of images into 2-D using
dimensionality reduction techniques (e.g., t-SNE). However, neither approach
effectively scales to large datasets because images are ineffectively organized
and interactions are insufficiently supported. To address these challenges, we
develop DendroMap by adapting Treemaps, a well-known visualization technique.
DendroMap effectively organizes images by extracting hierarchical cluster
structures from high-dimensional representations of images. It enables users to
make sense of the overall distributions of datasets and interactively zoom into
specific areas of interests at multiple levels of abstraction. Our case studies
with widely-used image datasets for deep learning demonstrate that users can
discover insights about datasets and trained models by examining the diversity
of images, identifying underperforming subgroups, and analyzing classification
errors. We conducted a user study that evaluates the effectiveness of DendroMap
in grouping and searching tasks by comparing it with a gridified version of
t-SNE and found that participants preferred DendroMap. DendroMap is available
at https://div-lab.github.io/dendromap/.
