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

Bertucci, Donald; Hamid, Md Montaser; Anand, Yashwanthi; Ruangrotsakun, Anita; Tabatabai, Delyar; Perez, Melissa; Kahng, Minsuk

doi:10.1109/tvcg.2022.3209425

Cited by 17 publications

(7 citation statements)

References 48 publications

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“…This hybrid research direction of combining visualization and ML has made significant contributions to model evaluation [41]. For different modeling tasks, tools for visualizing metrics (e.g., accuracy, precision, recall) on subsets of data [1,15,16,91] and tools for exploring large ML datasets [10,46] help practitioners compare and evaluate how well ML models generalize to unseen data. Example ML tasks incorporating visualization include data classification [3,36,70], image classification [19], object detection [34], transfer learning [56], and natural language processing (NLP) [13,43,79,80].…”

Section: Visualization For Model Evaluationmentioning

confidence: 99%

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

Hohman,

Wang,

Lee

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Section: Visualization For Model Evaluationmentioning

confidence: 99%

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

Hohman,

Wang,

Lee

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…PhotoMesa [Bed01] for more expressive zoom, as well as methods that target specific kinds of analysis in exploration, e.g. interactively refining a recommendation system [ZWVW20], or the study of image datasets as they pertain to training machine learning models [BHA*23]. These methods can offer a deeper analysis on image collections; nevertheless, our method is distinct from prior works in supporting exploration at the object‐level, rather than focusing on image‐level exploration.…”

Section: Related Workmentioning

confidence: 99%

CUPID: Contextual Understanding of Prompt‐conditioned Image Distributions

Zhao,

Li,

Berger

2024

Computer Graphics Forum

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We present CUPID: a visualization method for the contextual understanding of prompt‐conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text‐to‐image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high‐dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low‐dimensional space via density‐based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large‐scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes.

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“…There are many more powerful visualization types that could improve the usability of zeno. Instance views that encode semantic similarity, such as DendroMap [4], Facets [46], or AnchorViz [12], could improve users' ability to find patterns and new behaviors in their data. zeno can also adapt existing visualizations of ML performance, such as ML Cube [29], Neo [23], or ConfusionFlow [25], to better visualize model behaviors.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

Cabrera,

Fu,

Bertucci

et al. 2023

Preprint

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Figure 1: zeno is a framework for behavioral evaluation of machine learning (ML) models. It has two components, a Python API and an interactive UI. The API is used to generate information such as model outputs and metrics. Users then interact with the UI to see metrics, create slices, and write unit tests. In this toy example, a user is evaluating a cat and dog classifier. They see that the model has lower accuracy for dogs with pointy ears, and create a test expecting the slice accuracy to be higher than 70%.

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

Cited by 17 publications

References 48 publications

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

CUPID: Contextual Understanding of Prompt‐conditioned Image Distributions

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

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