# Visual Analytics For Machine Learning: A Data Perspective Survey

arXiv: https://arxiv.org/abs/2307.07712 · HTML: https://mlsanity.com/paper/2307.07712/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 95 of its 202 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 30 of 95)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [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)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models](https://mlsanity.com/paper/1804.09299/) (2018)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [Do Convolutional Neural Networks Learn Class Hierarchy?](https://mlsanity.com/paper/1710.06501/) (2017)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [FairSight: Visual Analytics for Fairness in Decision Making](https://mlsanity.com/paper/1908.00176/) (2019)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [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)
- [Towards Better Analysis of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.07043/) (2016)
- [Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models](https://mlsanity.com/paper/1808.00196/) (2018)
- [Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models](https://mlsanity.com/paper/2009.07053/) (2020)
- [Understanding Hidden Memories of Recurrent Neural Networks](https://mlsanity.com/paper/1710.10777/) (2017)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2008.08353/) (2020)
- [Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective](https://mlsanity.com/paper/1702.01226/) (2017)

## Built on by (most cited first, 25 of 25)

- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Empowering Time Series Forecasting with LLM-Agents](https://mlsanity.com/paper/2508.04231/) (2025)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding](https://mlsanity.com/paper/2401.06462/) (2024)
- [Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning](https://mlsanity.com/paper/2311.01016/) (2023)
- [Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments](https://mlsanity.com/paper/2408.03274/) (2024)
- [Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://mlsanity.com/paper/2508.12730/) (2025)
- [Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement](https://mlsanity.com/paper/2508.12555/) (2025)
- [Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards](https://mlsanity.com/paper/2604.21769/) (2026)
- [GenLens: A Systematic Evaluation of Visual GenAI Model Outputs](https://mlsanity.com/paper/2402.03700/) (2024)
- [A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regime](https://mlsanity.com/paper/2507.03866/) (2025)
- [NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models](https://mlsanity.com/paper/2509.03985/) (2025)
- [SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing](https://mlsanity.com/paper/2511.15182/) (2025)
- [KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models](https://mlsanity.com/paper/2603.29689/) (2026)
- [GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks](https://mlsanity.com/paper/2406.04548/) (2024)
- [Visual Attention Exploration in Vision-Based Mamba Models](https://mlsanity.com/paper/2502.20764/) (2025)
- [d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement](https://mlsanity.com/paper/2507.11960/) (2025)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities](https://mlsanity.com/paper/2603.05542/) (2026)
- [Visualization of Machine Learning Models through Their Spatial and Temporal Listeners](https://mlsanity.com/paper/2603.27527/) (2026)
- [VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs](https://mlsanity.com/paper/2604.23356/) (2026)
- [Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics](https://mlsanity.com/paper/2607.00969/) (2026)
- [FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction](https://mlsanity.com/paper/2607.27463/) (2026)
- [Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models](https://mlsanity.com/paper/2609.22097/) (2026)

## Abstract (arXiv)

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this survey. Since data quality greatly impacts the performance of ML models, our survey focuses specifically on summarizing VIS4ML works from the data perspective. First, we categorize the common data handled by ML models into five types, explain the unique features of each type, and highlight the corresponding ML models that are good at learning from them. Second, from the large number of VIS4ML works, we tease out six tasks that operate on these types of data (i.e., data-centric tasks) at different stages of the ML pipeline to understand, diagnose, and refine ML models. Lastly, by studying the distribution of 143 surveyed papers across the five data types, six data-centric tasks, and their intersections, we analyze the prospective research directions and envision future research trends.