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In this paper, we present DendroMap, a novel approach to interactivelyexploring large-scale image datasets for machine learning (ML). MLpractitioners often explore image datasets by generating a grid of images orprojecting high-dimensional representations of images into 2-D usingdimensionality reduction techniques (e.g., t-SNE). However, neither approacheffectively scales to large datasets because images are ineffectively organizedand interactions are insufficiently supported. To address these challenges, wedevelop DendroMap by adapting Treemaps, a well-known visualization technique.DendroMap effectively organizes images by extracting hierarchical clusterstructures from high-dimensional representations of images. It enables users tomake sense of the overall distributions of datasets and interactively zoom intospecific areas of interests at multiple levels of abstraction. Our case studieswith widely-used image datasets for deep learning demonstrate that users candiscover insights about datasets and trained models by examining the diversityof images, identifying underperforming subgroups, and analyzing classificationerrors. We conducted a user study that evaluates the effectiveness of DendroMapin grouping and searching tasks by comparing it with a gridified version oft-SNE and found that participants preferred DendroMap. DendroMap is availableat https://div-lab.github.io/dendromap/.

DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps