# Direct-Manipulation Visualization of Deep Networks

arXiv: https://arxiv.org/abs/1708.03788 · HTML: https://mlsanity.com/paper/1708.03788/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2017.

## Built on by (most cited first, 28 of 28)

- [M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis](https://mlsanity.com/paper/2107.08264/) (2021)
- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
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- [Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation](https://mlsanity.com/paper/2408.04619/) (2024)
- [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://mlsanity.com/paper/2302.14165/) (2023)
- [Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math Textbooks](https://mlsanity.com/paper/2307.16112/) (2023)
- [LossPlot: A Better Way to Visualize Loss Landscapes](https://mlsanity.com/paper/2111.15133/) (2021)
- [Interactive Analysis of CNN Robustness](https://mlsanity.com/paper/2110.07667/) (2021)
- [Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries](https://mlsanity.com/paper/2203.16475/) (2022)
- [EL-VIT: Probing Vision Transformer with Interactive Visualization](https://mlsanity.com/paper/2401.12666/) (2024)
- [From Tinkering to Engineering: Measurements in Tensorflow Playground](https://mlsanity.com/paper/2101.04141/) (2021)
- [FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks](https://mlsanity.com/paper/2204.04511/) (2022)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [Seizing the Means of Production: Exploring the Landscape of Crafting, Adapting and Navigating Generative AI Models in the Visual Arts](https://mlsanity.com/paper/2404.17688/) (2024)
- [Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits](https://mlsanity.com/paper/2305.19889/) (2023)
- [Quantum Machine Learning Playground](https://mlsanity.com/paper/2507.17931/) (2025)
- [Model-agnostic interpretation by visualization of feature perturbations](https://mlsanity.com/paper/2101.10502/) (2021)
- [On the Evolution of Neuron Communities in a Deep Learning Architecture](https://mlsanity.com/paper/2106.04693/) (2021)
- [Deep Learning Image Recognition for Non-images](https://mlsanity.com/paper/2106.14350/) (2021)
- [PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control](https://mlsanity.com/paper/2201.10257/) (2022)
- [Interactive Visual Feature Search](https://mlsanity.com/paper/2211.15060/) (2022)
- [Interactive Simulations of Backdoors in Neural Networks](https://mlsanity.com/paper/2405.13217/) (2024)
- [Sparsest Models Elude Pruning: An Expos'e of Pruning's Current Capabilities](https://mlsanity.com/paper/2407.04075/) (2024)
- [Federated Learning Playground](https://mlsanity.com/paper/2602.19489/) (2026)
- [Visualization of Machine Learning Models through Their Spatial and Temporal Listeners](https://mlsanity.com/paper/2603.27527/) (2026)
- [Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation](https://mlsanity.com/paper/2604.14668/) (2026)
- [Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning](https://mlsanity.com/paper/2604.17050/) (2026)
- [KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2610.20117/) (2026)

## Abstract (arXiv)

The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.