In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning. Machine learning 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 over the compared method.

本文提出了DendroMap这一新方法，可以有效地组织和交互式地探索大规模图像数据集，通过提取高维图像的层次聚类结构，用户可以在不同的抽象层级上对数据集进行总体分布的检查和兴趣区域的交互式缩放以及透视。研究结果表明，DendroMap可以帮助用户发现数据集和训练模型方面的见解，其效果优于t-SNE格子化版本，DendroMap已经在网站上提供。

DendroMap: 用Treemaps进行机器学习的大规模图像数据集的可视化探索