# GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

arXiv: https://arxiv.org/abs/2202.00965 · HTML: https://mlsanity.com/paper/2202.00965/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Semantic Photo Manipulation with a Generative Image Prior](https://mlsanity.com/paper/2005.07727/) (2020)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- [Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias](https://mlsanity.com/paper/1906.06439/) (2019)
- [A Survey of Inductive Biases for Factorial Representation-Learning](https://mlsanity.com/paper/1612.05299/) (2016)
- [Evaluating the Interpretability of Generative Models by Interactive Reconstruction](https://mlsanity.com/paper/2102.01264/) (2021)
- [A Comparative Analysis of Industry Human-AI Interaction Guidelines](https://mlsanity.com/paper/2010.11761/) (2020)
- [How to Support Users in Understanding Intelligent Systems? Structuring the Discussion](https://mlsanity.com/paper/2001.08301/) (2020)
- [crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity](https://mlsanity.com/paper/2008.05914/) (2020)
- [Mask-Guided Discovery of Semantic Manifolds in Generative Models](https://mlsanity.com/paper/2105.07273/) (2021)

## Built on by (most cited first, 13 of 13)

- [3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows](https://mlsanity.com/paper/2210.11603/) (2022)
- [PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions](https://mlsanity.com/paper/2308.05184/) (2023)
- [Next Steps for Human-Centered Generative AI: A Technical Perspective](https://mlsanity.com/paper/2306.15774/) (2023)
- [GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks](https://mlsanity.com/paper/2207.08320/) (2022)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networks](https://mlsanity.com/paper/2302.00079/) (2023)
- [BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI](https://mlsanity.com/paper/2403.13947/) (2024)
- [The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success](https://mlsanity.com/paper/2402.14728/) (2024)
- [Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering](https://mlsanity.com/paper/2505.04260/) (2025)
- [Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes](https://mlsanity.com/paper/2305.11927/) (2023)
- [GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization](https://mlsanity.com/paper/2601.18585/) (2026)
- [Design Generative AI for Practitioners: Exploring Interaction Approaches Aligned with Creative Practice](https://mlsanity.com/paper/2603.03074/) (2026)
- [Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces](https://mlsanity.com/paper/2608.25876/) (2026)

## Abstract (arXiv)

We investigate how multiple sliders with and without feedforward visualizations influence users' control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects.