# DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps arXiv: https://arxiv.org/abs/2205.06935 ยท HTML: https://mlsanity.com/paper/2205.06935/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 72 references that are in the map. - First posted on arXiv 14 May 2022. ## Builds on (most cited first, 25 of 25) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017) - [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017) - [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021) - [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019) - [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020) - [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018) - [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021) - [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019) - [Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations](https://mlsanity.com/paper/1904.02323/) (2019) - [ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models](https://mlsanity.com/paper/1704.01942/) (2017) - [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020) - [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016) - [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020) - [DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2008.08353/) (2020) - [ViCE: Visual Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2003.02428/) (2020) - [OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples](https://mlsanity.com/paper/2002.03103/) (2020) - [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021) - [Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models](https://mlsanity.com/paper/2108.03738/) (2021) - [Interactive Steering of Hierarchical Clustering](https://mlsanity.com/paper/2009.09618/) (2020) - [Crowdsourcing the Perception of Machine Teaching](https://mlsanity.com/paper/2002.01618/) (2020) - [II-20: Intelligent and pragmatic analytic categorization of image collections](https://mlsanity.com/paper/2005.02149/) (2020) - [Contrastive Identification of Covariate Shift in Image Data](https://mlsanity.com/paper/2108.08000/) (2021) - [ExplorerTree: a focus+context exploration approach for 2D embeddings](https://mlsanity.com/paper/2106.10592/) (2021) ## Built on by (most cited first, 12 of 12) - [PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2307.09036/) (2023) - [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023) - [Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation](https://mlsanity.com/paper/2403.12075/) (2024) - [Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms](https://mlsanity.com/paper/2306.08167/) (2023) - [A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision](https://mlsanity.com/paper/2308.05168/) (2023) - [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024) - [Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models](https://mlsanity.com/paper/2305.11364/) (2023) - [Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning](https://mlsanity.com/paper/2311.01016/) (2023) - [CUPID: Contextual Understanding of Prompt-conditioned Image Distributions](https://mlsanity.com/paper/2406.07699/) (2024) - [GenLens: A Systematic Evaluation of Visual GenAI Model Outputs](https://mlsanity.com/paper/2402.03700/) (2024) - [BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis](https://mlsanity.com/paper/2308.08003/) (2023) - [A Multi-Attribute Latent Space for Visual Analysis of Watches](https://mlsanity.com/paper/2606.27897/) (2026) ## Abstract (arXiv) 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/.